Weizhen Qi


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DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation
Wei Chen | Yeyun Gong | Song Wang | Bolun Yao | Weizhen Qi | Zhongyu Wei | Xiaowu Hu | Bartuer Zhou | Yi Mao | Weizhu Chen | Biao Cheng | Nan Duan
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Dialog response generation in open domain is an important research topic where the main challenge is to generate relevant and diverse responses. In this paper, we propose a new dialog pre-training framework called DialogVED, which introduces continuous latent variables into the enhanced encoder-decoder pre-training framework to increase the relevance and diversity of responses. With the help of a large dialog corpus (Reddit), we pre-train the model using the following 4 tasks, used in training language models (LMs) and Variational Autoencoders (VAEs) literature: 1) masked language model; 2) response generation; 3) bag-of-words prediction; and 4) KL divergence reduction. We also add additional parameters to model the turn structure in dialogs to improve the performance of the pre-trained model. We conduct experiments on PersonaChat, DailyDialog, and DSTC7-AVSD benchmarks for response generation. Experimental results show that our model achieves the new state-of-the-art results on all these datasets.


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GLGE: A New General Language Generation Evaluation Benchmark
Dayiheng Liu | Yu Yan | Yeyun Gong | Weizhen Qi | Hang Zhang | Jian Jiao | Weizhu Chen | Jie Fu | Linjun Shou | Ming Gong | Pengcheng Wang | Jiusheng Chen | Daxin Jiang | Jiancheng Lv | Ruofei Zhang | Winnie Wu | Ming Zhou | Nan Duan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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POS-Constrained Parallel Decoding for Non-autoregressive Generation
Kexin Yang | Wenqiang Lei | Dayiheng Liu | Weizhen Qi | Jiancheng Lv
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)

The multimodality problem has become a major challenge of existing non-autoregressive generation (NAG) systems. A common solution often resorts to sequence-level knowledge distillation by rebuilding the training dataset through autoregressive generation (hereinafter known as “teacher AG”). The success of such methods may largely depend on a latent assumption, i.e., the teacher AG is superior to the NAG model. However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation. To provide a feasible solution to the multimodality problem of NAG, we propose incorporating linguistic structure (Part-of-Speech sequence in particular) into NAG inference instead of relying on teacher AG. More specifically, the proposed POS-constrained Parallel Decoding (POSPD) method aims at providing a specific POS sequence to constrain the NAG model during decoding. Our experiments demonstrate that POSPD consistently improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation. This observation validates the necessity of exploring the alternatives for sequence-level knowledge distillation.

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ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
Weizhen Qi | Yeyun Gong | Yu Yan | Can Xu | Bolun Yao | Bartuer Zhou | Biao Cheng | Daxin Jiang | Jiusheng Chen | Ruofei Zhang | Houqiang Li | Nan Duan
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

Now, the pre-training technique is ubiquitous in natural language processing field. ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks. In this paper, we extend ProphetNet into other domains and languages, and present the ProphetNet family pre-training models, named ProphetNet-X, where X can be English, Chinese, Multi-lingual, and so on. We pre-train a cross-lingual generation model ProphetNet-Multi, a Chinese generation model ProphetNet-Zh, two open-domain dialog generation models ProphetNet-Dialog-En and ProphetNet-Dialog-Zh. And also, we provide a PLG (Programming Language Generation) model ProphetNet-Code to show the generation performance besides NLG (Natural Language Generation) tasks. In our experiments, ProphetNet-X models achieve new state-of-the-art performance on 10 benchmarks. All the models of ProphetNet-X share the same model structure, which allows users to easily switch between different models. We make the code and models publicly available, and we will keep updating more pre-training models and finetuning scripts.


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Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing
Bo Shao | Yeyun Gong | Weizhen Qi | Nan Duan | Xiaola Lin
Proceedings of the 28th International Conference on Computational Linguistics

In this paper, we present a multi-level alignment pretraining method in a unified architecture formulti-lingual semantic parsing. In this architecture, we use an adversarial training method toalign the space of different languages and use sentence level and word level parallel corpus assupervision information to align the semantic of different languages. Finally, we jointly train themulti-level alignment and semantic parsing tasks. We conduct experiments on a publicly avail-able multi-lingual semantic parsing dataset ATIS and a newly constructed dataset. Experimentalresults show that our model outperforms state-of-the-art methods on both datasets.

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XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation
Yaobo Liang | Nan Duan | Yeyun Gong | Ning Wu | Fenfei Guo | Weizhen Qi | Ming Gong | Linjun Shou | Daxin Jiang | Guihong Cao | Xiaodong Fan | Ruofei Zhang | Rahul Agrawal | Edward Cui | Sining Wei | Taroon Bharti | Ying Qiao | Jiun-Hung Chen | Winnie Wu | Shuguang Liu | Fan Yang | Daniel Campos | Rangan Majumder | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks. Comparing to GLUE (Wang et al.,2019), which is labeled in English and includes natural language understanding tasks only, XGLUE has three main advantages: (1) it provides two corpora with different sizes for cross-lingual pre-training; (2) it provides 11 diversified tasks that cover both natural language understanding and generation scenarios; (3) for each task, it provides labeled data in multiple languages. We extend a recent cross-lingual pre-trained model Unicoder (Huang et al., 2019) to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline. We also evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for comparison.

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ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training
Weizhen Qi | Yu Yan | Yeyun Gong | Dayiheng Liu | Nan Duan | Jiusheng Chen | Ruofei Zhang | Ming Zhou
Findings of the Association for Computational Linguistics: EMNLP 2020

This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of optimizing one-step-ahead prediction in the traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction that predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large-scale dataset (160GB), respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.


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Aggregating Bidirectional Encoder Representations Using MatchLSTM for Sequence Matching
Bo Shao | Yeyun Gong | Weizhen Qi | Nan Duan | Xiaola Lin
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In this work, we propose an aggregation method to combine the Bidirectional Encoder Representations from Transformer (BERT) with a MatchLSTM layer for Sequence Matching. Given a sentence pair, we extract the output representations of it from BERT. Then we extend BERT with a MatchLSTM layer to get further interaction of the sentence pair for sequence matching tasks. Taking natural language inference as an example, we split BERT output into two parts, which is from premise sentence and hypothesis sentence. At each position of the hypothesis sentence, both the weighted representation of the premise sentence and the representation of the current token are fed into LSTM. We jointly train the aggregation layer and pre-trained layer for sequence matching. We conduct an experiment on two publicly available datasets, WikiQA and SNLI. Experiments show that our model achieves significantly improvement compared with state-of-the-art methods on both datasets.