Yikang Shen

Unsupervised Dependency Graph Network

Peng Li

Jie Zhou

2022-05-01

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)

doi.org

Unsupervised Dependency Graph Network

Yikang Shen

Shawn Tan

Alessandro Sordoni

Peng Li

Jie Zhou

Aaron Courville

Recent work has identified properties of pretrained self-attention models that mirror those of dependency parse structures. In particular, s… (voir plus)ome self-attention heads correspond well to individual dependency types. Inspired by these developments, we propose a new competitive mechanism that encourages these attention heads to model different dependency relations. We introduce a new model, the Unsupervised Dependency Graph Network (UDGN), that can induce dependency structures from raw corpora and the masked language modeling task. Experiment results show that UDGN achieves very strong unsupervised dependency parsing performance without gold POS tags and any other external information. The competitive gated heads show a strong correlation with human-annotated dependency types. Furthermore, the UDGN can also achieve competitive performance on masked language modeling and sentence textual similarity tasks.

2022-01-01

Annual Meeting of the Association for Computational Linguistics (published)

doi.org

StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling

Yikang Shen

Yi Tay

Che Zheng

Dara Bahri

Donald Metzler

Aaron Courville

2021-08-01

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) (publié)

doi.org

openreview.net

Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle

Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization proble… (voir plus)ms and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with an incremental parser and maintains the conditional probability setting of a standard language model (left-to-right). To train the incremental parser and avoid exposure bias, we also propose a novel dynamic oracle, so that SOM is more robust to wrong parsing decisions. Experiments show that SOM can achieve strong results in language modeling, incremental parsing, and syntactic generalization tests while using fewer parameters than other models.

2021-06-01

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (publié)

doi.org

arxiv.org

Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension

Oliver Bender

Franz Josef Och

Yoshua Bengio

R´ejean Ducharme

Pascal Vincent

Kevin Clark

Quoc Minh-Thang Luong

V. Le

Jacob Devlin

Ming-Wei Chang

Kenton Lee

Adam Fisch

Alon Talmor

Robin Jia

Minjoon Seo

Michael R. Glass

A. Gliozzo

Rishav Chakravarti

Ian J Goodfellow

Jean Pouget-Abadie … (voir 39 de plus)

Mehdi Mirza

Serhii Havrylov

Ivan Titov. 2017

Emergence

Jun-Tao He

Jiatao Gu

Jiajun Shen

Marc’Aurelio

Matthew Henderson

I. Casanueva

Nikola Mrkˇsi´c

Pei-hao Su

Tsung-Hsien Wen

Ivan Vuli´c

Yikang Shen

Yi Tay

Che Zheng

Dara Bahri

Donald

Metzler Aaron

Courville

Structformer

Ashish Vaswani

Noam M. Shazeer

Niki Parmar

Thomas Wolf

Lysandre Debut

Julien Victor Sanh

Clement Chaumond

Anthony Delangue

Pier-339 Moi

Tim ric Cistac

R´emi Rault

Morgan Louf

Qizhe Xie

Eduard H. Hovy

Silei Xu

Sina Jandaghi Semnani

Giovanni Campagna

Pretrained language models have signiﬁcantly 001 improved the performance of down-stream 002 language understanding tasks, including ex-00… (voir plus)3 tractive question answering, by providing 004 high-quality contextualized word embeddings. 005 However, training question answering models 006 still requires large amounts of annotated data 007 for speciﬁc domains. In this work, we pro-008 pose a cooperative, self-play learning frame-009 work, REGEX, for automatically generating 010 more non-trivial question-answer pairs to im-011 prove model performance. REGEX is built 012 upon a masked answer extraction task with an 013 interactive learning environment containing an 014 answer entity REcognizer, a question Gener-015 ator, and an answer EXtractor. Given a pas-016 sage with a masked entity, the generator gen-017 erates a question around the entity, and the 018 extractor is trained to extract the masked en-019 tity with the generated question and raw texts. 020 The framework allows the training of question 021 generation and answering models on any text 022 corpora without annotation. We further lever-023 age a reinforcement learning technique to re-024 ward generating high-quality questions and to 025 improve the answer extraction model’s perfor-026 mance. Experiment results show that REGEX 027 outperforms the state-of-the-art (SOTA) pre-028 trained language models and transfer learning 029 approaches on standard question-answering 030 benchmarks, and yields the new SOTA per-031 formance under given model size and transfer 032 learning settings. 033

Learning Task Decomposition with Ordered Memory Policy Network

Yuchen Lu

Yikang Shen

Siyuan Zhou

Aaron Courville

Joshua B. Tenenbaum

Chuang Gan

Many complex real-world tasks are composed of several levels of sub-tasks. Humans leverage these hierarchical structures to accelerate the l… (voir plus)earning process and achieve better generalization. In this work, we study the inductive bias and propose Ordered Memory Policy Network (OMPN) to discover subtask hierarchy by learning from demonstration. The discovered subtask hierarchy could be used to perform task decomposition, recovering the subtask boundaries in an unstruc-tured demonstration. Experiments on Craft and Dial demonstrate that our modelcan achieve higher task decomposition performance under both unsupervised and weakly supervised settings, comparing with strong baselines. OMPN can also bedirectly applied to partially observable environments and still achieve higher task decomposition performance. Our visualization further confirms that the subtask hierarchy can emerge in our model.

2021-01-01

ICLR (publié)

openreview.net

Recursive Top-Down Production for Sentence Generation with Latent Trees

2020-11-01

Findings of the Association for Computational Linguistics: EMNLP 2020 (publié)

doi.org

arxiv.org

Explicitly Modeling Syntax in Language Model improves Generalization

Syntax is fundamental to our thinking about language. Although neural networks are very successful in many tasks, they do not explicitly mod… (voir plus)el syntactic structure. Failing to capture the structure of inputs could lead to generalization problems and over-parametrization. In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM). The model explicitly models the structure with a one-step look-ahead parser and maintains the conditional probability setting of the standard language model. Experiments show that SOM can achieve strong results in language modeling and syntactic generalization tests, while using fewer parameters then other models.

2020-10-21

arXiv.org (prépublication)

dblp.uni-trier.de

Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach

Wenyu Du

Yue Sara Zhang

It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally eff… (voir plus)iciently incorporating syntactic structure into neural language models has been a challenging topic. In this paper, we make use of a multi-task objective, i.e., the models simultaneously predict words as well as ground truth parse trees in a form called “syntactic distances”, where information between these two separate objectives shares the same intermediate representation. Experimental results on the Penn Treebank and Chinese Treebank datasets show that when ground truth parse trees are provided as additional training signals, the model is able to achieve lower perplexity and induce trees with better quality.

2020-07-01

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (publié)

Ordered Memory

Seyedarian Hosseini

2019-10-29

ArXiv (prépublication)

Ordered Memory

Seyedarian Hosseini

Stack-augmented recurrent neural networks (RNNs) have been of interest to the deep learning community for some time. However, the difficulty… (voir plus) of training memory models remains a problem obstructing the widespread use of such models. In this paper, we propose the Ordered Memory architecture. Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of the memory. We also introduce a new Gated Recursive Cell to compose lower-level representations into higher-level representation. We demonstrate that our model achieves strong performance on the logical inference task (Bowman et al., 2015) and the ListOps (Nangia and Bowman, 2018) task. We can also interpret the model to retrieve the induced tree structure, and find that these induced structures align with the ground truth. Finally, we evaluate our model on the Stanford Sentiment Treebank tasks (Socher et al., 2013), and find that it performs comparatively with the state-of-the-art methods in the literature.

2019-10-29

ArXiv (prépublication)

arxiv.org