A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations

We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model's ability to selectively focus on the relevant parts of an input sequence.Comment: To appear at ACL2017, code available at https://github.com/sronnqvist/discourse-ablst

A PDTB-Styled End-to-End Discourse Parser

We have developed a full discourse parser in the Penn Discourse Treebank (PDTB) style. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies their relation types. When appropriate, the attribution spans to these relations are also determined. We present a comprehensive evaluation from both component-wise and error-cascading perspectives.Comment: 15 pages, 5 figures, 7 table

接続詞の必要箇所の自動判定―文章添削システムの構築に向けて―

文章を作成する能力というのは正確に物事を伝える上でとても重要である．その際，自然な文章にするためには適切な接続詞が必要である．我々は文章の自動添削システムを構築することを最終的な目標とし，その中でも特に判定が困難である，「論理関係の適切さ」の判別を行う．そこで，様々な観点からどのような場合に接続詞が必要であるかを分析することで，任意の文書を与えた時に接続詞を必要とする箇所を自動的に判定する手法が必要であり，本研究ではその手法の開発を目的とする．本研究では機械学習の分類器を用いて，接続詞がどのような文間で必要になるかを自動的に推定する．接続詞が必要となる箇所は必須箇所と推奨箇所の2 つに区別することができると考える．そのため必須，推奨箇所について独立に分類器を構築する．使用した属性は助詞（9 種類），助動詞（9 種類），繰り返し語句（名詞，動詞），シソーラス距離，文節パタン情報，係り受けのパタン情報である．必須箇所では，決定木では適合率が高い結果が得られた．そのため，分類器が接続詞が必要である箇所と判断するものに対しては正しい結果を得られると言える．一方，SVM では決定木よりは再現率が高いものの，適合率は低くなった．そのため，添削システムとして用いるのであればSVM よりも決定木を用いる方が有効である事がわかった．推奨箇所では，決定木では必須箇所同様に「省略できない」と分類されたものに関しては正しく分類出来た．これは，適合率がベースラインよりも高い結果から有効である事がわかった．文章添削システムに向けて，既存の研究である接続関係の同定と本研究の分類器を適用する前後での分類性能の結果を出した．この結果を見ると「並列」，「累加」，「転換」，「逆接」に関しては分類性能が上昇したため，本研究が有効に働いたと考えられる．電気通信大学201

Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue

Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community's ability to develop better theories, as well as good off the shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic independent way. Our results show that our theoretically motivated features achieve 66% accuracy, an improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue}, author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference}, year={2013}

On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification

Implicit discourse relation classification is one of the most difficult parts in shallow discourse parsing as the relation prediction without explicit connectives requires the language understanding at both the text span level and the sentence level. Previous studies mainly focus on the interactions between two arguments. We argue that a powerful contextualized representation module, a bilateral multi-perspective matching module, and a global information fusion module are all important to implicit discourse analysis. We propose a novel model to combine these modules together. Extensive experiments show that our proposed model outperforms BERT and other state-of-the-art systems on the PDTB dataset by around 8% and CoNLL 2016 datasets around 16%. We also analyze the effectiveness of different modules in the implicit discourse relation classification task and demonstrate how different levels of representation learning can affect the results.Comment: Accepted by IJCAI 202