Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS). However, these systems heavily depend on many hand-crafted linguistic features. In this paper, we propose a discourse context-aware self-attention neural network model for fine-grained IS classification. On the ISNotes corpus (Markert et al., 2012), our model with the contextually-encoded word representations (BERT) (Devlin et al., 2018) achieves new state-of-the-art performances on fine-grained IS classification, obtaining a 4.1% absolute overall accuracy improvement compared to Hou et al. (2013a). More importantly, we also show an improvement of 3.9% F1 for bridging anaphora recognition without using any complex hand-crafted semantic features designed for capturing the bridging phenomenon.
East China Normal University（华东师范大学）；Shanghai Transsion Co., Ltd.（上海传音控股股份有限公司）
Due to the absence of connectives, implicit discourse relation recognition (IDRR) is still a challenging and crucial task in discourse analysis. Most of the current work adopted multi-task learning to aid IDRR through explicit discourse relation recognition (EDRR) or utilized dependencies between discourse relation labels to constrain model predictions. But these methods still performed poorly on fine-grained IDRR and even utterly misidentified on most of the few-shot discourse relation classes. To address these problems, we propose a novel Prompt-based Connective Prediction (PCP) method for I…
Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS). However, these systems heavily depend on many hand-crafted linguistic features. In this paper, we propose a simple discourse context-aware BERT model for fine-grained IS classification. On the ISNotes corpus (Markert et al., 2012), our model achieves new state-of-the-art performance on fine-grained IS classification, obtaining a 4.8 absolute overall accuracy improvement compared to Hou et al. (2013a). More importantly, we also show an improvement o…
Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context -- both document and sentence level information -- than prior work. We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets.
We test the hypothesis that discourse predictability influences Hindi syntactic choice. While prior work has shown that a number of factors (e.g., information status, dependency length, and syntactic surprisal) influence Hindi word order preferences, the role of discourse predictability is underexplored in the literature. Inspired by prior work on syntactic priming, we investigate how the words and syntactic structures in a sentence influence the word order of the following sentences. Specifically, we extract sentences from the Hindi-Urdu Treebank corpus (HUTB), permute the preverbal constitue…
Xiaomi AI Lab（小米人工智能实验室）；The Hong Kong Polytechnic University（香港理工大学）
To model diverse responses for a given post, one promising way is to introduce a latent variable into Seq2Seq models. The latent variable is supposed to capture the discourse-level information and encourage the informativeness of target responses. However, such discourse-level information is often too coarse for the decoder to be utilized. To tackle it, our idea is to transform the coarse-grained discourse-level information into fine-grained word-level information. Specifically, we firstly measure the semantic concentration of corresponding target response on the post words by introducing a fi…
Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface features from the input sentence pairs. Thus, properly representing the text is very crucial to this task. In this paper, we propose a model augmented with different grained text representations, including character, subword, word, sentence, and sentence pair levels. The proposed deeper model is evaluated on the benchmark treebank and achieves state-of-the-art accuracy with greater …
Yelp（Yelp公司）；University of Saarland（萨尔大学）；Brandeis University（布兰迪斯大学）
Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Surface features achieve good performance, but they are not readily applicable to other languages without semantic lexicons. Previous neural models require parses, surface features, or a small label set to work well. Here, we propose neural network models that are based on feedforward and long-short term memory architecture without any surface features. To our surprise, our best configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning…
When Discourse Pressures Conflict: Information Structure in Vision-Language Model Outputs
Department of Computer Science, Aalborg University（奥尔堡大学计算机科学系）；Department of Psycholinguistics and Neurolinguistics, ELTE Research Centre for Linguistics（ELTE语言研究中心心理学语言学与神经语言学系）；ELTE Bárczi Gusztáv Faculty of Special Needs Education（ELTE巴尔茨吉斯塔夫特殊教育学院）
From Context to EDUs: Faithful and Structured Context Compression via Elementary Discourse Unit Decomposition
DeepLang AI；Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Jiaotong University（北京交通大学）
Most previous studies on information status (IS) classification and bridging anaphora recognition assume that the gold mention or syntactic tree information is given (Hou et al., 2013; Roesiger et al., 2018; Hou, 2020; Yu and Poesio, 2020). In this paper, we propose an end-to-end neural approach for information status classification. Our approach consists of a mention extraction component and an information status assignment component. During the inference time, our system takes a raw text as the input and generates mentions together with their information status. On the ISNotes corpus (Marker…
Suggestion mining tasks are often semantically complex and lack sophisticated methodologies that can be applied to real-world data. The presence of suggestions across a large diversity of domains and the absence of large labelled and balanced datasets render this task particularly challenging to deal with. In an attempt to overcome these challenges, we propose a two-tier pipeline that leverages Discourse Marker based oversampling and fine-grained suggestion mining techniques to retrieve suggestions from online forums. Through extensive comparison on a real-world open-domain suggestion dataset,…
RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension
Xinjiang University（新疆大学）；Renmin University of China（中国人民大学）；Anhui University（安徽大学）；Sun Yat-sen University（中山大学）；Tsinghua University（清华大学）；University of Southampton（南安普顿大学）
Indiana University Purdue University Indianapolis（印第安纳大学与普渡大学印第安纳波里斯联合分校）；Qatar Computing Research Institute – HBKU（卡塔尔计算研究所 – HBKU）；University of Mississippi（密西西比大学）
Vector representation of sentences is important for many text processing tasks that involve clustering, classifying, or ranking sentences. Recently, distributed representation of sentences learned by neural models from unlabeled data has been shown to outperform the traditional bag-of-words representation. However, most of these learning methods consider only the content of a sentence and disregard the relations among sentences in a discourse by and large. In this paper, we propose a series of novel models for learning latent representations of sentences (Sen2Vec) that consider the content of …
Institute for Language, Cognition and Computation（语言、认知与计算研究所）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text segments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervision. We introduce an attention-based polarity scoring method for identifying positive and negative text snippets and a new dataset which we call SPOT (as shorthand for Segment-level POlariTy annotations) for evaluating MIL-style sentiment models like ours. Experimental results demonstrate s…
Qualicen GmbH（Qualicen有限公司）；Technical University of Munich（慕尼黑工业大学）；Blekinge Institute of Technology（布莱金厄理工学院）；University of Cologne（科隆大学）；fortiss GmbH（福蒂斯有限公司）
[Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases from requirements, automatically extracting such causal statements are a basic necessity. [Problem:] We lack an approach that is able to extract causal relations from natural language requirements in fine-grained form. Specifically, existing approaches do not consider the combinatorics between causes and effects. They also do not allow to split causes and effects into more granular text fragments (e.g., variable and …
Renmin University of China（中国人民大学）；Xiaomi AI Lab（小米人工智能实验室）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Applying existing methods to emotional support conversation -- which provides valuable assistance to people who are in need -- has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user's instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user's distress. To address the problems, we propose a novel model \textbf{MISC}, which firstly infers the user's fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental resul…
Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates
Bielefeld University（比勒费尔德大学）；TU Darmstadt（达姆施塔特工业大学）；Hans-Bredow-Institut（汉斯·布雷多研究所）；University of Mannheim（曼海姆大学）；University of Technology Nuremberg（纽伦堡工业大学）
Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method
Lab of Artificial Intelligence for Education, East China Normal University（教育人工智能实验室，华东师范大学）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（教育人工智能上海研究院，华东师范大学）；School of Computer Science and Technology, East China Normal University（计算机科学与技术学院，华东师范大学）；Department of Chinese Language and Literature, East China Normal University（中文语言文学系，华东师范大学）；College of Education, Zhejiang University（教育学院，浙江大学）
