UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

Abstract

We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM). Given an input text with masked tokens, we rely on conventional masks to learn inter-relations between corrupted tokens and context via autoencoding, and pseudo masks to learn intra-relations between masked spans via partially autoregressive modeling. With well-designed position embeddings and self-attention masks, the context encodings are reused to avoid redundant computation. Moreover, conventional masks used for autoencoding provide global masking information, so that all the position embeddings are accessible in partially autoregressive language modeling. In addition, the two tasks pre-train a unified language model as a bidirectional encoder and a sequence-to-sequence decoder, respectively. Our experiments show that the unified language models pre-trained using PMLM achieve new state-of-the-art results on a wide range of natural language understanding and generation tasks across several widely used benchmarks.

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1 Introduction

Language model (LM) pre-training on large-scale text corpora has substantially advanced the state of the art across a variety of natural language processing tasks Peters et al. (2018); Radford et al. (2018); Devlin et al. (2018); Dong et al. (2019); Liu et al. (2019); Yang et al. (2019); Lewis et al. (2019); Lan et al. (2019); Raffel et al. (2019). After LM pre-training, the obtained model can be fine-tuned to various downstream tasks.

Figure 1: Given input , the tokens are masked by the special tokens [M] and [P]. For each example, we jointly train two types of LMs, namely, autoencoding (AE), and partially autoregressive (PAR) masked LMs.

Two types of language model pre-training objectives are commonly employed to learn contextualized text representations by predicting words conditioned on their context. The first strand of work relies on autoencoding LMs Devlin et al. (2018); Liu et al. (2019). For example, the masked language modeling task used by BERT Devlin et al. (2018) randomly masks some tokens in a text sequence, and then independently recovers the masked tokens by conditioning on the encoding vectors obtained by a bidirectional Transformer Vaswani et al. (2017). The second type of pre-training uses autoregressive modeling Radford et al. (2018); Lewis et al. (2019); Yang et al. (2019); Raffel et al. (2019). Rather than independently predicting words, the probability of a word is dependent on previous predictions.

Inspired by Dong et al. (2019), we propose a pseudo-masked language model (PMLM) to jointly pre-train a bidirectional LM for language understanding (e.g., text classification, and question answering) and a sequence-to-sequence LM for language generation (e.g., document summarization, and response generation). Specifically, the bidirectional model is pre-trained by autoencoding (AE) LMs, and the sequence-to-sequence model is pre-trained by partially autoregressive (PAR) LMs. As shown in Figure 1, the model parameters are shared in two language modeling tasks, and the encoding results of the given context tokens are reused. We use the conventional mask [MASK] (or [M] for short) to represent the corrupted tokens for AE pre-training. In order to handle factorization steps of PAR language modeling, we append pseudo masks [Pseudo] (or [P] for short) to the input sequence without discarding the original tokens. With well-designed self-attention masks and position embeddings, the PMLM can perform the two language modeling tasks in one forward pass without redundant computation of context.

The proposed method has the following advantages. First, the PMLM pre-trains different LMs in a unified manner, which learns both inter-relations between masked tokens and given context (via AE), and intra-relations between masked spans (via PAR). Moreover, conventional masks used for AE provide global masking information, so that every factorization step of PAR pre-training can access all the position embeddings as in fine-tuning. Second, the unified pre-training framework learns models for both natural language understanding and generation Dong et al. (2019). Specifically, the AE-based modeling learns a bidirectional Transformer encoder, and the PAR objective pre-trains a sequence-to-sequence decoder. Third, the proposed model is computationally efficient in that the AE and PAR modeling can be computed in one forward pass. Because the encoding results of given context are reused for two language modeling tasks, redundant computation is avoided. Fourth, PAR language modeling learns token-to-token, token-to-span, and span-to-span relations during pre-training. By taking spans (i.e., continuous tokens) into consideration, PMLM is encouraged to learn long-distance dependencies by preventing local shortcuts.

We conduct PMLM pre-training on large-scale text corpora. Then we fine-tune the pre-trained model to a wide range of natural language understanding and generation tasks. Experimental results show that unified pre-training using PMLM improves performance on various benchmarks.

2 Preliminary

2.1 Backbone Network: Transformer

First, we pack the embeddings of input tokens together into . Then stacked Transformer Vaswani et al. (2017) blocks compute the encoding vectors via:

(1)

where is the number of layers. The hidden vectors of the final layer are the contextualized representations of input. Within each Transformer block, multiple self-attention heads aggregate the output vectors of the previous layer, followed by a fully-connected feed-forward network.

Self-Attention Masks The output of a self-attention head in the -th Transformer layer is:

(2)

where parameters project the previous layer’s output to queries, keys, and values, respectively. It is worth noting that the mask matrix controls whether two tokens can attend each other.

2.2 Input Representation

The inputs of language model pre-training are sequences sampled from large-scale text corpora. We follow the format used by BERT Devlin et al. (2018). We add a special start-of-sequence token [SOS] at the beginning to get the representation of the whole input. Besides, each text is split into two segments appended with a special end-of-sequence token [EOS]. The final input format is “[SOS] S1 [EOS] S2 [EOS]”, where the segments S1 and S2 are contiguous texts. The vector of an input token is represented by the summation of its token embedding, absolute position embedding, and segment embedding. All the embedding vectors are obtained by lookup in learnable matrices.

3 Unified Language Model Pre-Training

Figure 2: Overview of PMLM pre-training. The model parameters are shared across the LM objectives. The bidirectional LM is trained by autoencoding MLM, and the sequence-to-sequence (Seq-to-Seq) LM is trained by partially autoregressive MLM. We use different self-attention masks to control the access to context for each word token.

We propose a pseudo-masked language model (PMLM) to jointly pre-train both autoencoding (Section 3.1.1) and partially autoregressive (Section 3.1.2) LMs. As shown in Figure 2, PMLM reuses the encoding results of the same example to jointly pre-train both modeling methods by pseudo masking (Section 3.2).

3.1 Pre-Training Tasks

Factorization Order Probability of Masked Tokens
Autoencoding (e.g., BERT, and our work)
Autoregressive (e.g., GPT, and XLNet)
Partially Autoregressive (our work)
Table 1: Given input , the tokens are masked. We compare how to compute with different factorization orders for autoencoding, autoregressive, and partially autoregressive masked language models.

We use the masked language modeling (MLM; Devlin et al. 2018) task to pre-train a Transformer network, which is also known as the cloze task Taylor (1953). For a given input, we randomly substitute tokens with a special token [MASK] (or [M] for short). The training objective is to recover them by conditioning on the output hidden states of Transformer.

As shown in Table 1, we categorize MLMs into autoencoding, autoregressive, and partially autoregressive. Their main difference is how the probability of masked tokens is factorized. In our work, we leverage autoencoding (AE) and partially autoregressive (PAR) modeling for pre-training, which is formally described as follows. It is worth noting that the masked positions are the same for both AE and PAR modeling, but the probability factorization is different.

Autoencoding Modeling

The autoencoding method independently predicts the tokens by conditioning on context, which is the same as BERT. Given original input and the positions of masks , the probability of masked tokens is computed by , where , is set minus, means all input tokens except the ones that are in . The autoencoding pre-training loss is defined as:

(3)

where is the training corpus.

Partially Autoregressive Modeling

We propose to pre-train partially autoregressive MLMs. In each factorization step, the model can predict one or multiple tokens. Let denote factorization order, where is the set of mask positions in the -th factorization step. If all factorization steps only contain one masked token (i.e., ), the modeling becomes autoregressive. In our work, we enable a factorization step to be a span, which makes the LM partially autoregressive. The probability of masked tokens is decomposed as:

(4)
(5)

where , and . The partially autoregressive pre-training loss is defined as:

(6)

where is the expectation over the factorization distribution. During pre-training, we randomly sample one factorization order for each input text Yang et al. (2019), rather than computing the exact expectation.

  Input : Input sequence
  Output : Masked positions
  
  repeat
      rand_int()\hfill Randomly sample an index
      rand_int()  if rand() 0.4 else 1
     if  has not been masked then
         
  until \hfill Masking ratio is
  return
Algorithm 1 Blockwise Masking

Blockwise Masking and Factorization Given input sequence , the masking policy uniformly produces a factorization order for Equation (6). For the -th factorization step, the masked position set contains one token, or a continuous text span Joshi et al. (2019). As described in Algorithm 1, we randomly sample of the original tokens as masked tokens. Among them, of the time we mask a -gram block, and of the time we mask a token. We then construct a factorization step with the set of masked positions. We repeat the above process until enough masked tokens are sampled. The randomly sampled factorization orders are similar to permutation-based language modeling used by XLNet Yang et al. (2019). However, XLNet only emits predictions one by one (i.e., autoregressive). In contrast, we can generate one token, or a text span at each factorization step (i.e., partially autoregressive).

3.2 Pseudo-Masked LM

Figure 3: Comparisons between autoencoding (AE), autoregressive (AR), and partially autoregressive (PAR) masked language models. In the example , the tokens are masked by the special tokens [M] and [P].

Equation (5) indicates that factorization steps of partially autoregressive language modeling are conditioned on different context. So if masked language models Devlin et al. (2018) are directly used, we have to construct a new cloze instance (as shown in Figure 3) for each factorization step, which renders partially autoregressive pre-training infeasible. We propose a new training procedure, named as pseudo-masked language model (PMLM), to overcome the issue.

Figure 4: Example of the factorization steps . The masks [P] and [M] are assigned with the same position embeddings as the corresponding tokens. Different context is used to compute the hidden states for the pseudo masks of and .

For the last example in Table 1, Figure 4 shows how the PMLM conducts partially autoregressive predictions. Rather than replacing the tokens with masks as in vanilla MLMs, we keep all original input tokens unchanged and append pseudo masks to the input sequence. For each masked token, we insert a [Pseudo] (or [P] for short) token with the same position embedding of the corresponding token. The top-layer hidden states of [P] tokens are fed into a softmax classifier for MLM predictions. Notice that positional information in Transformer is encoded by (absolute) position embeddings, while the model components are order-agnostic. In other words, no matter where a token appears in the input sequence, the position of the token is only determined by its position embedding. So we can assign the same position embedding to two tokens, and Transformer treats both of the tokens as if they have the same position.

Vanilla MLMs allow all tokens to attend to each other, while PMLM controls accessible context for each token according to the factorization order. As shown in Figure 4, the example’s factorization order is . When we compute , only and the pseudo masks of are conditioned on. The original tokens of are masked to avoid information leakage, while their pseudo tokens [P] are used as placeholders for MLM predictions. In the second step, the tokens and the pseudo mask of are conditioned on to compute . Unlike in the first step, the original tokens of are used for the prediction.

Figure 5: Self-attention mask of the factorization steps . Both conventional masks [M] and given context () can be attended by all the tokens.

Self-attention masks (as described in Section 2.1) are used to control what context a token can attend to when computing its contextualized representation. Figure 5 shows the self-attention mask matrix used for the example of Figure 4. The self-attention mask matrix is designed in order to avoid two kinds of information leakage. The first type is explicit leakage, i.e., the masked token can be directly accessed by its pseudo token, which renders the LM prediction trivial. So pseudo tokens [P] are not allowed to attend to the content of “themselves” in a PMLM. The second type is implicit leakage, which implicitly leaks prediction information by multi-step attention propagations. For example, as shown in Figure 5, if the context token has access to , there is a connected attention flow “’s pseudo mask token ”, which eases the prediction of . As a result, for each token, we mask the attentions to the tokens that are predicted in the future factorization steps.

Model SQuAD v1.1 SQuAD v2.0 F1 EM F1 EM  BERT 88.5 80.8 76.3 73.7  XLNet - - - 80.2  RoBERTa 91.5 84.6 83.7 80.5 UniLMv2 93.1 87.1 86.1 83.3    – rel pos 93.0 86.7 85.2 82.4
Table 2: Results of base-size pre-trained models on the SQuAD v1.1/v2.0 development sets. We report F1 and exact match (EM) scores. Results of UniLMv2 are averaged over five runs. “– rel pos” is the model without relative position bias.
\hfill Model MNLI SST-2 MRPC RTE QNLI QQP STS CoLA Acc Acc Acc Acc Acc Acc PCC MCC  BERT 84.5 93.2 87.3 68.6 91.7 91.3 89.5 58.9  XLNet 86.8 94.7 88.2 74.0 91.7 91.4 89.5 60.2  RoBERTa 87.6 94.8 90.2 78.7 92.8 91.9 91.2 63.6 UniLMv2 88.5 95.1 91.8 81.3 93.5 91.7 91.0 65.2    – rel pos 88.4 95.0 91.2 78.1 93.4 91.8 91.2 63.8
Table 3: Results of base-size models on the development set of the GLUE benchmark. We report Matthews correlation coefficient (MCC) for CoLA, Pearson correlation coefficient (PCC) for STS, and accuracy (Acc) for the rest. Metrics of UniLMv2 are averaged over five runs for the tasks. “– rel pos” is the ablation model without relative position bias.

3.3 Unified Pre-Training

As shown in Figure 2, we jointly pre-train bidirectional and sequence-to-sequence LMs with the same input text and masked positions. Both the special tokens [M] and [P] emit predicted tokens. The training objective is to maximize the likelihood of correct tokens, which considers two types of LMs (i.e., autoencoding, and partially autoregressive) in one example. The loss is computed via:

(7)

where are defined as in Equation (3), and Equation (6) respectively. The proposed method sufficiently reuses the computed hidden states for both LM objectives. In addition, experiments in Section 4.6 show that the pre-training tasks are complementary to each other, as they capture both inter- (i.e., between given context and masked tokens) and intra- (i.e., among masked tokens) relations of the input tokens.

3.4 Fine-tuning on NLU and NLG Tasks

Following Dong et al. (2019), we fine-tune the pre-trained PMLM (with additional task-specific layers if necessary) to both natural language understanding (NLU) and natural language generation (NLG) tasks.

For NLU tasks, we fine-tune PMLM as a bidirectional Transformer encoder, like BERT. Let us take text classification as an example. Similar to the text format described in Section 2.2, the input is “[SOS] TEXT [EOS]”. We use the encoding vector of [SOS] as the representation of input, and then feed it to a randomly initialized softmax classifier (i.e., the task-specific output layer). We maximize the likelihood of the labeled training data by updating the parameters of the pre-trained PMLM and the added softmax classifier.

For sequence-to-sequence generation tasks, the example is concatenated as “[SOS] SRC [EOS] TGT [EOS]”, where SRC and TGT are source and target sequences, respectively. The fine-tuning procedure is similar to pre-training as in Section 3.2. For a source sequence, the dependencies between the tokens are bidirectional, i.e., all the source tokens can attend to each other. In contrast, the target sequence is produced in an autoregressive manner. So we append a pseudo mask [P] for each target token, and use self-attention masks to perform autoregressive generation. The fine-tuning objective is to maximize the likelihood of the target sequence given source input. It is worth noting that [EOS] is used to mark the end of the target sequence. Once [EOS] is emitted, we terminate the generation process of the target sequence. During decoding, we use beam search to generate the target tokens one by one Dong et al. (2019).

Model #Param CNN/DailyMail XSum
RG-1 RG-2 RG-L RG-1 RG-2 RG-L
Without pre-training
Lead-3 40.42 17.62 36.67 16.30 1.60 11.95
PtrNet See et al. (2017) 39.53 17.28 36.38 28.10 8.02 21.72
Fine-tuning large-size pre-trained models
UniLM Dong et al. (2019) 340M 43.08 20.43 40.34 - - -
BART Lewis et al. (2019) 400M 44.16 21.28 40.90 45.14 22.27 37.25
T5 Raffel et al. (2019) 11B 43.52 21.55 40.69 - - -
Fine-tuning base-size pre-trained models
MASS Song et al. (2019) 123M 42.12 19.50 39.01 39.75 17.24 31.95
BERTSumAbs Liu and Lapata (2019) 156M 41.72 19.39 38.76 38.76 16.33 31.15
T5 Raffel et al. (2019) 220M 42.05 20.34 39.40 - - -
UniLMv2 110M 43.16 20.42 40.14 44.00 21.11 36.08
  – relative position bias 110M 43.45 20.71 40.49 43.69 20.71 35.73
Table 4: Abstractive summarization results on CNN/DailyMail and XSum. The evaluation metric is the F1 version of ROUGE (RG) scores. We also present the number of parameters (#Param) for the methods using pre-trained models.
#Param BLEU-4 MTR RG-L
Du and Cardie (2018) 15.16 19.12 -
Zhang and Bansal (2019) 18.37 22.65 46.68
UniLM 340M 22.78 25.49 51.57
UniLMv2 110M 24.43 26.34 51.97
  – rel pos 110M 24.70 26.33 52.13
Zhao et al. (2018) 16.38 20.25 44.48
Zhang and Bansal (2019) 20.76 24.20 48.91
UniLM 340M 24.32 26.10 52.69
UniLMv2 110M 26.29 27.16 53.22
  – rel pos 110M 26.30 27.09 53.19
Table 5: Results on question generation. The first block follows the data split in Du and Cardie (2018), while the second block is the same as in Zhao et al. (2018). MTR is short for METEOR, and RG for ROUGE. “#Param” indicates the size of pre-trained models. “– rel pos” is the model without relative position bias.

4 Experimental Results

We employ pseudo-masked language model to conduct unified language model pre-training (UniLMv2), and fine-tuned the model on both natural language understanding (i.e., question answering, the GLUE benchmark) and generation (i.e., abstractive summarization, and question generation) tasks. Details about hyperparameters and datasets can be found in the supplementary material. In addition, we conducted ablation studies to compare different choices of pre-training objectives.

4.1 Pre-Training Setup

We followed the same model size as BERT Devlin et al. (2018) for comparison purposes. Specifically, we used a -layer Transformer with attention heads. The hidden size was , and inner hidden size of feed-forward network was . The weight matrix of the softmax classifier was tied with the token embedding matrix. We also add relative position bias Raffel et al. (2019) to attention scores. The whole model contains about M parameters.

For fair comparisons, we report the major results using similar pre-training datasets and optimization hyperparameters as in RoBERTa Liu et al. (2019). We use 160GB text corpora from English Wikipedia, BookCorpus Zhu et al. (2015), OpenWebText1, CC-News Liu et al. (2019), and Stories Trinh and Le (2018). We follow the preprocess and the uncased WordPiece Wu et al. (2016) tokenization used in Devlin et al. (2018). The vocabulary size was . The maximum length of input sequence was . The token masking probability was . Among masked positions, of the time we replaced the token with masks, of the time with a random token, and keeping the original token for the rest. The block masking (see Algorithm 1) can mask up to -gram for one factorization step in partially autoregressive modeling. The batch size was set to . We used Adam Kingma and Ba (2015) with , , and 1e-6 for optimization. The peak learning rate was set to 6e-4, with linear warmup over the first steps and linear decay. The weight decay was . The dropout rate was set to . We ran the pre-training procedure for million steps, which took about days using Nvidia V100-32GB GPU cards.

Model Objective SQuAD v1.1 SQuAD v2.0 MNLI SST-2
F1 EM F1 EM m mm Acc
BERT AE 88.5 80.8 76.3 73.7 84.3 84.7 92.8
XLNet AR - - 81.0 78.2 85.6 85.1 93.4
RoBERTa AE 90.6 - 79.7 - 84.7 - 92.7
BART AR 90.8 - - - 83.8 - -
[1] UniLMv2 AE+PAR 92.0 85.6 83.6 80.9 86.1 86.1 93.2
[2] [1] – relative position bias AE+PAR 91.5 85.0 81.8 78.9 85.6 85.5 93.0
[3] [2] – blockwise factorization AE+AR 90.8 84.1 80.7 77.8 85.4 85.5 92.6
[4] [2] – PAR AE 91.0 84.2 81.3 78.4 84.9 85.0 92.4
[5] [2] – AE PAR 90.7 83.9 79.9 77.0 84.9 85.2 92.5
[6] [5] – blockwise factorization AR 89.9 82.9 79.3 76.1 84.8 85.0 92.3
Table 6: Comparisons between the pre-training objectives. All models are pre-trained over Wikipedia and BookCorpus for one million steps with a batch size of . Results in the second block are average over five runs for each task. We report F1 and exact match (EM) scores for SQuAD, and accuracy (Acc) for MNLI and SST-2.

4.2 Question Answering

Question answering aims at returning answers for the given question and documents. We conduct experiments on the benchmarks SQuAD v1.1 Rajpurkar et al. (2016) and v2.0 Rajpurkar et al. (2018). The model learns to extract answer spans within a passage. We formulate the task as a natural language understanding problem. The input is concatenated as “[SOS] Question [EOS] Passage [EOS]”. We add a classification layer on the pre-trained PMLM, which predicts whether each token is the start or end position of an answer span by conditioning on the final outputs of Transformer. For SQuAD v2.0, we use the output vector of [SOS] to predict whether the instance is unanswerable or not.

The fine-tuning results are presented in Table 3, where we report F1 scores and exact match (EM) scores. We compare previous base-size models with PMLM. Notice that the publicly available BERT checkpoint Devlin et al. (2018) is pre-trained on 13GB corpora with batch size, while XLNet and RoBERTa are more directly comparable. The results show that UniLMv2 achieves better performance than the other models on both SQuAD datasets.

4.3 GLUE Benchmark

The General Language Understanding Evaluation (GLUE) benchmark Wang et al. (2019) contains various tasks. There are two single-sentence classification tasks, i.e., linguistic acceptability (CoLA; Warstadt et al. 2018), and sentiment analysis (SST-2; Socher et al. 2013). The text similarity (STS; Cer et al. 2017) task is formulated as a regression problem. The other tasks are pairwise classification tasks, including natural language inference (RTE, MNLI; Dagan et al. 2006; Bar-Haim et al. 2006; Giampiccolo et al. 2007; Bentivogli et al. 2009; Williams et al. 2018), question answering (QNLI; Rajpurkar et al. 2016), and paraphrase detection (QQP, MRPC; Dolan and Brockett 2005).

Table 3 presents the results on GLUE. We compare PMLM with three strong pre-trained models, i.e., BERT Devlin et al. (2018), XLNet Yang et al. (2019), and RoBERTa Liu et al. (2019), in the single task fine-tuning setting. All the models are in base-size for fair comparisons. We observe that the proposed UniLMv2 outperforms both BERT and XLNet across tasks. Comparing to state-of-the-art pre-trained RoBERTa, UniLMv2 obtains the best performance on out of tasks, e.g., vs (RoBERTa) in terms of MNLI accuracy, indicating the effectiveness of our UniLMv2.

4.4 Abstractive Summarization

We evaluate the pre-trained PMLM on two abstractive summarization datasets, i.e., XSum Narayan et al. (2018), and the non-anonymized version of CNN/DailyMail See et al. (2017). This is a language generation task, where the texts (such as news articles) are shortened to readable summaries that preserve salient information of the original texts. The pre-trained PMLM is fine-tuned as a sequence-to-sequence model as described in Section 3.4.

We report ROUGE scores Lin (2004) on the datasets. Table 4 shows two baseline methods that do not rely on pre-training. Lead-3 uses the first three input sentences as the summary. PtrNet See et al. (2017) is a sequence-to-sequence model with pointer networks. Results indicate that pre-training achieves significant improvements over the baselines. We also compare UniLMv2 with state-of-the-art pre-trained models of both base-size and large-size. We focus on the comparisons in the third block because the models contain similar numbers of parameters. BERTSumAbs Liu and Lapata (2019) fine-tunes a BERT encoder that is pre-trained with an autoencoding objective, concatenating with a randomly initialized decoder. MASS Song et al. (2019) and T5 Raffel et al. (2019) pre-train encoder-decoder Transformers with masked LM, which relies on the autoregressive pre-training. Although PMLM has the smallest size, we find that UniLMv2 outperforms the other base-size pre-trained models on both datasets.

4.5 Question Generation

We perform evaluations on question generation Du and Cardie (2018), the task of automatically producing relevant questions that ask for the given answer and context. The input of the sequence-to-sequence problem is defined as the concatenation of a paragraph and an answer. We fine-tune the pre-trained PMLM to predict output questions.

As shown in Table 5, we report BLEU Papineni et al. (2002), METEORBanerjee and Lavie (2005), and ROUGE Lin (2004) scores on two different data splits. Among the compared results, UniLM Dong et al. (2019) is based on pre-trained models, while the other three methods are sequence-to-sequence models enhanced with manual features Du and Cardie (2018), gated self-attention Zhao et al. (2018), and reinforcement learning Zhang and Bansal (2019). Results show that UniLMv2 achieves better evaluation metrics compared with UniLM and several baselines. It is worth noting that UniLMv2 consists of three times fewer parameters than UniLM.

4.6 Effect of Pre-Training Objectives

We conduct ablation experiments on using PMLM to implement different pre-training objectives, i.e., autoencoding (AE), autoregressive (AR), partially autoregressive (PAR), and jointly training (AE+AR, and AE+PAR). The evaluations follow the same settings2 as in BERT Devlin et al. (2018), so that the results in Table 6 can be directly compared with each other. Notice that XLNet Yang et al. (2019) is an autoregressive MLM augmented with more advanced relative position embeddings, and long-context memory.

As shown in Table 6, we compare the PMLM-based variants against previous models on question answering (SQuAD; Rajpurkar et al. 2016, 2018), natural language inference (MNLI; Williams et al. 2018), and sentiment classification (SST-2; Socher et al. 2013). First, we ablate relative position bias to better compare with BERT, RoBERTa, and BART. On text classification (MNLI and SST-2), the PAR-only objective compares favorably with both AE-only and AR-only objectives, which indicates the effectiveness of the proposed PAR modeling. In comparison, the SQuAD tasks require more precise modeling of spans in order to extract correct answer spans from the input passage, where both AE-only and PAR-only objectives outperform the AR-only objective. The results indicate that block masking and factorization are important for LM pre-training. Besides, the settings of jointly training (AE+AR, and AE+PAR) tend to improve the results over using single LM task. Among the five objectives, AE+PAR performs the best with the help of PMLM, which shows that autoencoding and partially autoregressive modelings are complementary for pre-training.

5 Conclusion

We pre-train a unified language model for language understanding and generation by joint learning bidirectional LM (via AE) and sequence-to-sequence LM (via PAR). We introduce a pseudo-masked language model (PMLM) to efficiently realize the unified pre-training procedure. PMLM is computationally efficient in that AE and PAR can be computed in one forward pass without redundant computation. Besides, the two modeling tasks are complementary to each other. Because conventional masks of AE provide global masking information to PAR, and PAR can learn intra-relations between masked spans. In addition, the proposed PAR pre-training encourages to learn long-distance dependencies by preventing local shortcuts. Experimental results show that PMLM improves the end-task results on several language understanding and generation benchmarks.

Appendix A Hyperparameters for Pre-Training

As shown in Table 7, we present the hyperparameters used for pre-training UniLMv2. We use the same WordPiece Wu et al. (2016) vocabulary and model size as BERT Devlin et al. (2018). We follow the optimization hyperparameters of RoBERTa Liu et al. (2019) for comparisons.

Layers 12
Hidden size 768
FFN inner hidden size 3072
Attention heads 12
Attention head size 64
Max relative position 128
Training steps 0.5M
Batch size 7680
Adam 1e-6
Adam (0.9, 0.98)
Learning rate 6e-4
Learning rate schedule Linear
Warmup ratio 0.048
Gradient clipping 0.0
Dropout 0.1
Weight decay 0.01
Table 7: Hyperparameters for pre-training UniLMv2.

Appendix B GLUE Benchmark

Table 8 summarizes the datasets used for the General Language Understanding Evaluation (GLUE) benchmark Wang et al. (2019).

Dataset #Train/#Dev/#Test
Single-Sentence Classification
CoLA (Acceptability) 8.5k/1k/1k
SST-2 (Sentiment) 67k/872/1.8k
Pairwise Text Classification
MNLI (NLI) 393k/20k/20k
RTE (NLI) 2.5k/276/3k
QNLI (NLI) 105k/5.5k/5.5k
WNLI (NLI) 634/71/146
QQP (Paraphrase) 364k/40k/391k
MRPC (Paraphrase) 3.7k/408/1.7k
Text Similarity
STS-B (Similarity) 7k/1.5k/1.4k
Table 8: Summary of the GLUE benchmark.

Appendix C Hyperparameters for NLU Fine-Tuning

Table 9 reports the hyperparameters used for fine-tuning UniLMv2 over SQuAD v1.10 Rajpurkar et al. (2016) / v2.0 Rajpurkar et al. (2018), and the GLUE benchmark Wang et al. (2019). The hyperparameters are searched on the development sets according to the average performance of five runs. We use the same hyperparameters for both SQuAD question answering datasets. Moreover, we list the hyperparameters used for the GLUE datasets in Table 9.

SQuAD v1.1/v2.0 GLUE
 Batch size 32 {16, 32}
 Learning rate 2e-5 {5e-6, 1e-5, 1.5e-5, 2e-5, 3e-5}
 LR schedule Linear
 Warmup ratio 0.1 {0.1, 0.2}
 Weight decay 0.01 {0.01, 0.1}
 Epochs 4 {10, 15}
Table 9: Hyperparameters used for fine-tuning on SQuAD, and GLUE.

Appendix D Hyperparameters for NLG Fine-Tuning

As shown in Table 10, we present the hyperparameters used for the natural language generation datasets, i.e., CNN/DailyMail See et al. (2017), XSum Narayan et al. (2018), and SQuAD question generation (QG; Du and Cardie 2018; Zhao et al. 2018). The total length is set to for QG, and for CNN/DailyMail and XSum. The maximum output length is set to for CNN/DailyMail, and for XSum and QG. The label smoothing Szegedy et al. (2016) rate is . During decoding, we use beam search to generate the outputs. Length penalty Wu et al. (2016) is also used to score candidates.

CNN/DailyMail XSum QG
Fine-Tuning
Learning rate 7e-5 7e-5 2e-5
Batch size 64 64 48
Weight decay 0.01
Epochs 14 14 16
Learning rate schedule Linear
Warmup ratio 0.02 0.02 0.1
Label smoothing 0.1
Max input length 608 720 464
Max output length 160 48 48
Decoding
Length penalty 0.7 0.6 1.3
Beam size 5 5 8
Table 10: Hyperparameters used for fine-tuning and decoding on CNN/DailyMail, XSum, and question generation (QG).

Footnotes

  1. skylion007.github.io/OpenWebTextCorpus
  2. Models were trained for 1M steps with batch size of over English Wikipedia and BookCorpus Zhu et al. (2015). The learning rate of Adam (, ) was set to 1e-4, with linear schedule and warmup over the first K steps.

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