Richard Yuanzhe Pang


2022

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Amortized Noisy Channel Neural Machine Translation
Richard Yuanzhe Pang | He He | Kyunghyun Cho
Proceedings of the 15th International Conference on Natural Language Generation

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QuALITY: Question Answering with Long Input Texts, Yes!
Richard Yuanzhe Pang | Alicia Parrish | Nitish Joshi | Nikita Nangia | Jason Phang | Angelica Chen | Vishakh Padmakumar | Johnny Ma | Jana Thompson | He He | Samuel Bowman
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average length of about 5,000 tokens, much longer than typical current models can process. Unlike in prior work with passages, our questions are written and validated by contributors who have read the entire passage, rather than relying on summaries or excerpts. In addition, only half of the questions are answerable by annotators working under tight time constraints, indicating that skimming and simple search are not enough to consistently perform well. Our baseline models perform poorly on this task (55.4%) and significantly lag behind human performance (93.5%).

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Token Dropping for Efficient BERT Pretraining
Le Hou | Richard Yuanzhe Pang | Tianyi Zhou | Yuexin Wu | Xinying Song | Xiaodan Song | Denny Zhou
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Transformer-based models generally allocate the same amount of computation for each token in a given sequence. We develop a simple but effective “token dropping” method to accelerate the pretraining of transformer models, such as BERT, without degrading its performance on downstream tasks. In particular, we drop unimportant tokens starting from an intermediate layer in the model to make the model focus on important tokens more efficiently if with limited computational resource. The dropped tokens are later picked up by the last layer of the model so that the model still produces full-length sequences. We leverage the already built-in masked language modeling (MLM) loss to identify unimportant tokens with practically no computational overhead. In our experiments, this simple approach reduces the pretraining cost of BERT by 25% while achieving similar overall fine-tuning performance on standard downstream tasks.

2021

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AgreeSum: Agreement-Oriented Multi-Document Summarization
Richard Yuanzhe Pang | Adam Lelkes | Vinh Tran | Cong Yu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Comparing Test Sets with Item Response Theory
Clara Vania | Phu Mon Htut | William Huang | Dhara Mungra | Richard Yuanzhe Pang | Jason Phang | Haokun Liu | Kyunghyun Cho | Samuel R. Bowman
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recent years have seen numerous NLP datasets introduced to evaluate the performance of fine-tuned models on natural language understanding tasks. Recent results from large pretrained models, though, show that many of these datasets are largely saturated and unlikely to be able to detect further progress. What kind of datasets are still effective at discriminating among strong models, and what kind of datasets should we expect to be able to detect future improvements? To measure this uniformly across datasets, we draw on Item Response Theory and evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models, while SNLI, MNLI, and CommitmentBank seem to be saturated for current strong models. We also observe span selection task format, which is used for QA datasets like QAMR or SQuAD2.0, is effective in differentiating between strong and weak models.

2020

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ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation
Lifu Tu | Richard Yuanzhe Pang | Sam Wiseman | Kevin Gimpel
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose to train a non-autoregressive machine translation model to minimize the energy defined by a pretrained autoregressive model. In particular, we view our non-autoregressive translation system as an inference network (Tu and Gimpel, 2018) trained to minimize the autoregressive teacher energy. This contrasts with the popular approach of training a non-autoregressive model on a distilled corpus consisting of the beam-searched outputs of such a teacher model. Our approach, which we call ENGINE (ENerGy-based Inference NEtworks), achieves state-of-the-art non-autoregressive results on the IWSLT 2014 DE-EN and WMT 2016 RO-EN datasets, approaching the performance of autoregressive models.

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Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?
Yada Pruksachatkun | Jason Phang | Haokun Liu | Phu Mon Htut | Xiaoyi Zhang | Richard Yuanzhe Pang | Clara Vania | Katharina Kann | Samuel R. Bowman
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-rich intermediate task, before fine-tuning it on a target task. However, it is still poorly understood when and why intermediate-task training is beneficial for a given target task. To investigate this, we perform a large-scale study on the pretrained RoBERTa model with 110 intermediate-target task combinations. We further evaluate all trained models with 25 probing tasks meant to reveal the specific skills that drive transfer. We observe that intermediate tasks requiring high-level inference and reasoning abilities tend to work best. We also observe that target task performance is strongly correlated with higher-level abilities such as coreference resolution. However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks. We also observe evidence that the forgetting of knowledge learned during pretraining may limit our analysis, highlighting the need for further work on transfer learning methods in these settings.

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Consistency of a Recurrent Language Model With Respect to Incomplete Decoding
Sean Welleck | Ilia Kulikov | Jaedeok Kim | Richard Yuanzhe Pang | Kyunghyun Cho
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition. We study the related issue of receiving infinite-length sequences from a recurrent language model when using common decoding algorithms. To analyze this issue, we first define inconsistency of a decoding algorithm, meaning that the algorithm can yield an infinite-length sequence that has zero probability under the model. We prove that commonly used incomplete decoding algorithms – greedy search, beam search, top-k sampling, and nucleus sampling – are inconsistent, despite the fact that recurrent language models are trained to produce sequences of finite length. Based on these insights, we propose two remedies which address inconsistency: consistent variants of top-k and nucleus sampling, and a self-terminating recurrent language model. Empirical results show that inconsistency occurs in practice, and that the proposed methods prevent inconsistency.

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Improving Joint Training of Inference Networks and Structured Prediction Energy Networks
Lifu Tu | Richard Yuanzhe Pang | Kevin Gimpel
Proceedings of the Fourth Workshop on Structured Prediction for NLP

Deep energy-based models are powerful, but pose challenges for learning and inference (Belanger and McCallum, 2016). Tu and Gimpel (2018) developed an efficient framework for energy-based models by training “inference networks” to approximate structured inference instead of using gradient descent. However, their alternating optimization approach suffers from instabilities during training, requiring additional loss terms and careful hyperparameter tuning. In this paper, we contribute several strategies to stabilize and improve this joint training of energy functions and inference networks for structured prediction. We design a compound objective to jointly train both cost-augmented and test-time inference networks along with the energy function. We propose joint parameterizations for the inference networks that encourage them to capture complementary functionality during learning. We empirically validate our strategies on two sequence labeling tasks, showing easier paths to strong performance than prior work, as well as further improvements with global energy terms.

2019

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Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation
Richard Yuanzhe Pang
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

Regarding the problem of automatically generating paraphrases with modified styles or attributes, the difficulty lies in the lack of parallel corpora. Numerous advances have been proposed for the generation. However, significant problems remain with the auto-evaluation of style transfer tasks. Based on the summary of Pang and Gimpel (2018) and Mir et al. (2019), style transfer evaluations rely on three metrics: post-transfer style classification accuracy, content or semantic similarity, and naturalness or fluency. We elucidate the dangerous current state of style transfer auto-evaluation research. Moreover, we propose ways to aggregate the three metrics into one evaluator. This abstract aims to bring researchers to think about the future of style transfer and style transfer evaluation research.

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Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer
Richard Yuanzhe Pang | Kevin Gimpel
Proceedings of the 3rd Workshop on Neural Generation and Translation

We consider the problem of automatically generating textual paraphrases with modified attributes or properties, focusing on the setting without parallel data (Hu et al., 2017; Shen et al., 2017). This setting poses challenges for evaluation. We show that the metric of post-transfer classification accuracy is insufficient on its own, and propose additional metrics based on semantic preservation and fluency as well as a way to combine them into a single overall score. We contribute new loss functions and training strategies to address the different metrics. Semantic preservation is addressed by adding a cyclic consistency loss and a loss based on paraphrase pairs, while fluency is improved by integrating losses based on style-specific language models. We experiment with a Yelp sentiment dataset and a new literature dataset that we propose, using multiple models that extend prior work (Shen et al., 2017). We demonstrate that our metrics correlate well with human judgments, at both the sentence-level and system-level. Automatic and manual evaluation also show large improvements over the baseline method of Shen et al. (2017). We hope that our proposed metrics can speed up system development for new textual transfer tasks while also encouraging the community to address our three complementary aspects of transfer quality.