Wei Peng


2021

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Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings
Weixuan Wang | Wei Peng | Meng Zhang | Qun Liu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Neural Machine Translation (NMT) has shown a strong ability to utilize local context to disambiguate the meaning of words. However, it remains a challenge for NMT to leverage broader context information like topics. In this paper, we propose heterogeneous ways of embedding topic information at the sentence level into an NMT model to improve translation performance. Specifically, the topic information can be incorporated as pre-encoder topic embedding, post-encoder topic embedding, and decoder topic embedding to increase the likelihood of selecting target words from the same topic of the source sentence. Experimental results show that NMT models with the proposed topic knowledge embedding outperform the baselines on the English -> German and English -> French translation tasks.

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When does Further Pre-training MLM Help? An Empirical Study on Task-Oriented Dialog Pre-training
Qi Zhu | Yuxian Gu | Lingxiao Luo | Bing Li | Cheng Li | Wei Peng | Minlie Huang | Xiaoyan Zhu
Proceedings of the Second Workshop on Insights from Negative Results in NLP

Further pre-training language models on in-domain data (domain-adaptive pre-training, DAPT) or task-relevant data (task-adaptive pre-training, TAPT) before fine-tuning has been shown to improve downstream tasks’ performances. However, in task-oriented dialog modeling, we observe that further pre-training MLM does not always boost the performance on a downstream task. We find that DAPT is beneficial in the low-resource setting, but as the fine-tuning data size grows, DAPT becomes less beneficial or even useless, and scaling the size of DAPT data does not help. Through Representational Similarity Analysis, we conclude that more data for fine-tuning yields greater change of the model’s representations and thus reduces the influence of initialization.

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HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management
Silin Gao | Ryuichi Takanobu | Wei Peng | Qun Liu | Minlie Huang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Robustness Testing of Language Understanding in Task-Oriented Dialog
Jiexi Liu | Ryuichi Takanobu | Jiaxin Wen | Dazhen Wan | Hongguang Li | Weiran Nie | Cheng Li | Wei Peng | Minlie Huang
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)

Most language understanding models in task-oriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution. However, these models can lead to system failure or undesirable output when being exposed to natural language perturbation or variation in practice. In this paper, we conduct comprehensive evaluation and analysis with respect to the robustness of natural language understanding models, and introduce three important aspects related to language understanding in real-world dialog systems, namely, language variety, speech characteristics, and noise perturbation. We propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness issues in task-oriented dialog. Four data augmentation approaches covering the three aspects are assembled in LAUG, which reveals critical robustness issues in state-of-the-art models. The augmented dataset through LAUG can be used to facilitate future research on the robustness testing of language understanding in task-oriented dialog.

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IIE-NLP-Eyas at SemEval-2021 Task 4: Enhancing PLM for ReCAM with Special Tokens, Re-Ranking, Siamese Encoders and Back Translation
Yuqiang Xie | Luxi Xing | Wei Peng | Yue Hu
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper introduces our systems for all three subtasks of SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning. To help our model better represent and understand abstract concepts in natural language, we well-design many simple and effective approaches adapted to the backbone model (RoBERTa). Specifically, we formalize the subtasks into the multiple-choice question answering format and add special tokens to abstract concepts, then, the final prediction of QA is considered as the result of subtasks. Additionally, we employ many finetuning tricks to improve the performance. Experimental results show that our approach gains significant performance compared with the baseline systems. Our system achieves eighth rank (87.51%) and tenth rank (89.64%) on the official blind test set of subtask 1 and subtask 2 respectively.

2020

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metaCAT: A Metadata-based Task-oriented Chatbot Annotation Tool
Ximing Liu | Wei Xue | Qi Su | Weiran Nie | Wei Peng
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations

Creating high-quality annotated dialogue corpora is challenging. It is essential to develop practical annotation tools to support humans in this time-consuming and error-prone task. We present metaCAT, which is an open-source web-based annotation tool designed specifically for developing task-oriented dialogue data. To the best of our knowledge, metaCAT is the first annotation tool that provides comprehensive metadata annotation coverage to the domain, intent, and span information. The data annotation quality is enhanced by a real-time annotation constraint-checking mechanism. An Automatic Speech Recognition (ASR) function is implemented to allow users to paraphrase and create more diversified annotated utterances. metaCAT is publicly available for the community.

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BLiMP: A Benchmark of Linguistic Minimal Pairs for English
Alex Warstadt | Alicia Parrish | Haokun Liu | Anhad Mohananey | Wei Peng | Sheng-Fu Wang | Samuel R. Bowman
Proceedings of the Society for Computation in Linguistics 2020

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BLiMP: The Benchmark of Linguistic Minimal Pairs for English
Alex Warstadt | Alicia Parrish | Haokun Liu | Anhad Mohananey | Wei Peng | Sheng-Fu Wang | Samuel R. Bowman
Transactions of the Association for Computational Linguistics, Volume 8

We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands.

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Huawei’s Submissions to the WMT20 Biomedical Translation Task
Wei Peng | Jianfeng Liu | Minghan Wang | Liangyou Li | Xupeng Meng | Hao Yang | Qun Liu
Proceedings of the Fifth Conference on Machine Translation

This paper describes Huawei’s submissions to the WMT20 biomedical translation shared task. Apart from experimenting with finetuning on domain-specific bitexts, we explore effects of in-domain dictionaries on enhancing cross-domain neural machine translation performance. We utilize a transfer learning strategy through pre-trained machine translation models and extensive scope of engineering endeavors. Four of our ten submissions achieve state-of-the-art performance according to the official automatic evaluation results, namely translation directions on English<->French, English->German and English->Italian.

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Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning
Haluk Açarçiçek | Talha Çolakoğlu | Pınar Ece Aktan Hatipoğlu | Chong Hsuan Huang | Wei Peng
Proceedings of the Fifth Conference on Machine Translation

This paper illustrates Huawei’s submission to the WMT20 low-resource parallel corpus filtering shared task. Our approach focuses on developing a proxy task learner on top of a transformer-based multilingual pre-trained language model to boost the filtering capability for noisy parallel corpora. Such a supervised task also helps us to iterate much more quickly than using an existing neural machine translation system to perform the same task. After performing empirical analyses of the finetuning task, we benchmark our approach by comparing the results with past years’ state-of-theart records. This paper wraps up with a discussion of limitations and future work. The scripts for this study will be made publicly available.

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IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE
Luxi Xing | Yuqiang Xie | Yue Hu | Wei Peng
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation. To clarify the intention for judgment and inject contrastive information for selection, we propose the input reconstruction strategy with prompt templates. Specifically, we formalize the subtasks into the multiple-choice question answering format and construct the input with the prompt templates, then, the final prediction of question answering is considered as the result of subtasks. Experimental results show that our approaches achieve significant performance compared with the baseline systems. Our approaches secure the third rank on both official test sets of the first two subtasks with an accuracy of 96.4 and an accuracy of 94.3 respectively.

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Bi-directional CognitiveThinking Network for Machine Reading Comprehension
Wei Peng | Yue Hu | Luxi Xing | Yuqiang Xie | Jing Yu | Yajing Sun | Xiangpeng Wei
Proceedings of the 28th International Conference on Computational Linguistics

We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory. It aims to simulate two ways of thinking in the brain to answer questions, including reverse thinking and inertial thinking. To validate the effectiveness of our framework, we design a corresponding Bi-directional Cognitive Thinking Network (BCTN) to encode the passage and generate a question (answer) given an answer (question) and decouple the bi-directional knowledge. The model has the ability to reverse reasoning questions which can assist inertial thinking to generate more accurate answers. Competitive improvement is observed in DuReader dataset, confirming our hypothesis that bi-directional knowledge helps the QA task. The novel framework shows an interesting perspective on machine reading comprehension and cognitive science.

2019

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Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs
Alex Warstadt | Yu Cao | Ioana Grosu | Wei Peng | Hagen Blix | Yining Nie | Anna Alsop | Shikha Bordia | Haokun Liu | Alicia Parrish | Sheng-Fu Wang | Jason Phang | Anhad Mohananey | Phu Mon Htut | Paloma Jeretic | Samuel R. Bowman
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Though state-of-the-art sentence representation models can perform tasks requiring significant knowledge of grammar, it is an open question how best to evaluate their grammatical knowledge. We explore five experimental methods inspired by prior work evaluating pretrained sentence representation models. We use a single linguistic phenomenon, negative polarity item (NPI) licensing, as a case study for our experiments. NPIs like any are grammatical only if they appear in a licensing environment like negation (Sue doesn’t have any cats vs. *Sue has any cats). This phenomenon is challenging because of the variety of NPI licensing environments that exist. We introduce an artificially generated dataset that manipulates key features of NPI licensing for the experiments. We find that BERT has significant knowledge of these features, but its success varies widely across different experimental methods. We conclude that a variety of methods is necessary to reveal all relevant aspects of a model’s grammatical knowledge in a given domain.

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Huawei’s NMT Systems for the WMT 2019 Biomedical Translation Task
Wei Peng | Jianfeng Liu | Liangyou Li | Qun Liu
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

This paper describes Huawei’s neural machine translation systems for the WMT 2019 biomedical translation shared task. We trained and fine-tuned our systems on a combination of out-of-domain and in-domain parallel corpora for six translation directions covering English–Chinese, English–French and English–German language pairs. Our submitted systems achieve the best BLEU scores on English–French and English–German language pairs according to the official evaluation results. In the English–Chinese translation task, our systems are in the second place. The enhanced performance is attributed to more in-domain training and more sophisticated models developed. Development of translation models and transfer learning (or domain adaptation) methods has significantly contributed to the progress of the task.