huashen218/convxai-cia-dataset huggingface dataset:
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.
Adversarial Attention Modeling for Multi-dimensional Emotion Regression
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: In this paper, we propose a neural network-based approach, namely Adversarial Attention Network, to the task of multi-dimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text. Especially, to determine which words are valuable for a particular emotion dimension, an attention layer is trained to weight the words in an input sequence. Furthermore, adversarial training is employed between two attention layers to learn better word weights via a discriminator. In particular, a shared attention layer is incorporated to learn public word weights between two emotion dimensions. Empirical evaluation on the EMOBANK corpus shows that our approach achieves notable improvements in r-values on both EMOBANK Reader{'}s and Writer{'}s multi-dimensional emotion regression tasks in all domains over the state-of-the-art baselines.
Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affective Computing
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: We propose a general strategy named {`}divide, conquer and combine{'} for multimodal fusion. Instead of directly fusing features at holistic level, we conduct fusion hierarchically so that both local and global interactions are considered for a comprehensive interpretation of multimodal embeddings. In the {`}divide{'} and {`}conquer{'} stages, we conduct local fusion by exploring the interaction of a portion of the aligned feature vectors across various modalities lying within a sliding window, which ensures that each part of multimodal embeddings are explored sufficiently. On its basis, global fusion is conducted in the {`}combine{'} stage to explore the interconnection across local interactions, via an Attentive Bi-directional Skip-connected LSTM that directly connects distant local interactions and integrates two levels of attention mechanism. In this way, local interactions can exchange information sufficiently and thus obtain an overall view of multimodal information. Our method achieves state-of-the-art performance on multimodal affective computing with higher efficiency.
Modeling Financial Analysts' Decision Making via the Pragmatics and Semantics of Earnings Calls
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target recommendations, which are used in equity re- search reports to help investors make deci- sions. In this paper, we examine analysts{'} decision making behavior as it pertains to the language content of earnings calls. We identify a set of 20 pragmatic features of analysts{'} questions which we correlate with analysts{'} pre-call investor recommendations. We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts{'} post-call changes in price targets. Our results show that earnings calls are moderately predictive of analysts{'} decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions. A breakdown of model errors indicates disparate performance on calls from different market sectors.
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence. This task is usually done in a pipeline manner, with aspect term extraction performed first, followed by sentiment predictions toward the extracted aspect terms. While easier to develop, such an approach does not fully exploit joint information from the two subtasks and does not use all available sources of training information that might be helpful, such as document-level labeled sentiment corpus. In this paper, we propose an interactive multi-task learning network (IMN) which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level. Unlike conventional multi-task learning methods that rely on learning common features for the different tasks, IMN introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables. Experimental results demonstrate superior performance of the proposed method against multiple baselines on three benchmark datasets.
Decompositional Argument Mining: A General Purpose Approach for Argument Graph Construction
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: This work presents an approach decomposing propositions into four functional components and identify the patterns linking those components to determine argument structure. The entities addressed by a proposition are target concepts and the features selected to make a point about the target concepts are aspects. A line of reasoning is followed by providing evidence for the points made about the target concepts via aspects. Opinions on target concepts and opinions on aspects are used to support or attack the ideas expressed by target concepts and aspects. The relations between aspects, target concepts, opinions on target concepts and aspects are used to infer the argument relations. Propositions are connected iteratively to form a graph structure. The approach is generic in that it is not tuned for a specific corpus and evaluated on three different corpora from the literature: AAEC, AMT, US2016G1tv and achieved an F score of 0.79, 0.77 and 0.64, respectively.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http://affective-meld.github.io.
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these problems, we propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. We further investigate three approaches under this framework, namely the pipeline, joint, and collapsed models. Experiments on three benchmark datasets show that our approach consistently outperforms the sequence tagging baseline. Moreover, we find that the pipeline model achieves the best performance compared with the other two models.
Transfer Capsule Network for Aspect Level Sentiment Classification
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence towards an aspect. Due to the high cost in annotation, the lack of aspect-level labeled data becomes a major obstacle in this area. On the other hand, document-level labeled data like reviews are easily accessible from online websites. These reviews encode sentiment knowledge in abundant contexts. In this paper, we propose a Transfer Capsule Network (TransCap) model for transferring document-level knowledge to aspect-level sentiment classification. To this end, we first develop an aspect routing approach to encapsulate the sentence-level semantic representations into semantic capsules from both the aspect-level and document-level data. We then extend the dynamic routing approach to adaptively couple the semantic capsules with the class capsules under the transfer learning framework. Experiments on SemEval datasets demonstrate the effectiveness of TransCap.
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: In aspect-level sentiment classification (ASC), it is prevalent to equip dominant neural models with attention mechanisms, for the sake of acquiring the importance of each context word on the given aspect. However, such a mechanism tends to excessively focus on a few frequent words with sentiment polarities, while ignoring infrequent ones. In this paper, we propose a progressive self-supervised attention learning approach for neural ASC models, which automatically mines useful attention supervision information from a training corpus to refine attention mechanisms. Specifically, we iteratively conduct sentiment predictions on all training instances. Particularly, at each iteration, the context word with the maximum attention weight is extracted as the one with active/misleading influence on the correct/incorrect prediction of every instance, and then the word itself is masked for subsequent iterations. Finally, we augment the conventional training objective with a regularization term, which enables ASC models to continue equally focusing on the extracted active context words while decreasing weights of those misleading ones. Experimental results on multiple datasets show that our proposed approach yields better attention mechanisms, leading to substantial improvements over the two state-of-the-art neural ASC models. Source code and trained models are available at https://github.com/DeepLearnXMU/PSSAttention.
Classification and Clustering of Arguments with Contextualized Word Embeddings
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater - Evidence Sentences dataset by 7.4 percentage points. For the understudied task of argument clustering, we propose a pre-training step which improves by 7.8 percentage points over strong baselines on a novel dataset, and by 12.3 percentage points for the Argument Facet Similarity (AFS) Corpus.
Sentiment Tagging with Partial Labels using Modular Architectures
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Many NLP learning tasks can be decomposed into several distinct sub-tasks, each associated with a partial label. In this paper we focus on a popular class of learning problems, sequence prediction applied to several sentiment analysis tasks, and suggest a modular learning approach in which different sub-tasks are learned using separate functional modules, combined to perform the final task while sharing information. Our experiments show this approach helps constrain the learning process and can alleviate some of the supervision efforts.
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction. The former task is to extract aspects of a product or service from an opinion document, and the latter is to identify the polarity expressed in the document about these extracted aspects. Most existing algorithms address them as two separate tasks and solve them one by one, or only perform one task, which can be complicated for real applications. In this paper, we treat these two tasks as two sequence labeling problems and propose a novel Dual crOss-sharEd RNN framework (DOER) to generate all aspect term-polarity pairs of the input sentence simultaneously. Specifically, DOER involves a dual recurrent neural network to extract the respective representation of each task, and a cross-shared unit to consider the relationship between them. Experimental results demonstrate that the proposed framework outperforms state-of-the-art baselines on three benchmark datasets.
A Corpus for Modeling User and Language Effects in Argumentation on Online Debating
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Existing argumentation datasets have succeeded in allowing researchers to develop computational methods for analyzing the content, structure and linguistic features of argumentative text. They have been much less successful in fostering studies of the effect of {``}user{''} traits {---} characteristics and beliefs of the participants {---} on the debate/argument outcome as this type of user information is generally not available. This paper presents a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. We also complete an example study using the dataset to analyze the effect of selected user traits on the debate outcome in comparison to the linguistic features typically employed in studies of this kind.
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations. Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topic-level relevance using multi-slice tensors. Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.
Learning from Omission
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Pragmatic reasoning allows humans to go beyond the literal meaning when interpret- ing language in context. Previous work has shown that such reasoning can improve the performance of already-trained language understanding systems. Here, we explore whether pragmatic reasoning during training can improve the quality of learned meanings. Our experiments on reference game data show that end-to-end pragmatic training produces more accurate utterance interpretation models, especially when data is sparse and language is complex.
Multi-Task Learning for Coherence Modeling
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: We address the task of assessing discourse coherence, an aspect of text quality that is essential for many NLP tasks, such as summarization and language assessment. We propose a hierarchical neural network trained in a multi-task fashion that learns to predict a document-level coherence score (at the network{'}s top layers) along with word-level grammatical roles (at the bottom layers), taking advantage of inductive transfer between the two tasks. We assess the extent to which our framework generalizes to different domains and prediction tasks, and demonstrate its effectiveness not only on standard binary evaluation coherence tasks, but also on real-world tasks involving the prediction of varying degrees of coherence, achieving a new state of the art.
Data Programming for Learning Discourse Structure
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: This paper investigates the advantages and limits of data programming for the task of learning discourse structure. The data programming paradigm implemented in the Snorkel framework allows a user to label training data using expert-composed heuristics, which are then transformed via the {``}generative step{''} into probability distributions of the class labels given the training candidates. These results are later generalized using a discriminative model. Snorkel{'}s attractive promise to create a large amount of annotated data from a smaller set of training data by unifying the output of a set of heuristics has yet to be used for computationally difficult tasks, such as that of discourse attachment, in which one must decide where a given discourse unit attaches to other units in a text in order to form a coherent discourse structure. Although approaching this problem using Snorkel requires significant modifications to the structure of the heuristics, we show that weak supervision methods can be more than competitive with classical supervised learning approaches to the attachment problem.
Evaluating Discourse in Structured Text Representations
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Discourse structure is integral to understanding a text and is helpful in many NLP tasks. Learning latent representations of discourse is an attractive alternative to acquiring expensive labeled discourse data. Liu and Lapata (2018) propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. We examine this model in detail, and evaluate on additional discourse-relevant tasks and datasets, in order to assess whether the structured attention improves performance on the end task and whether it captures a text{'}s discourse structure. We find the learned latent trees have little to no structure and instead focus on lexical cues; even after obtaining more structured trees with proposed model modifications, the trees are still far from capturing discourse structure when compared to discourse dependency trees from an existing discourse parser. Finally, ablation studies show the structured attention provides little benefit, sometimes even hurting performance.
Know What You Don't Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Zero-shot learning in Language {\&} Vision is the task of correctly labelling (or naming) objects of novel categories. Another strand of work in L{\&}V aims at pragmatically informative rather than {``}correct{''} object descriptions, e.g. in reference games. We combine these lines of research and model zero-shot reference games, where a speaker needs to successfully refer to a novel object in an image. Inspired by models of {``}rational speech acts{''}, we extend a neural generator to become a pragmatic speaker reasoning about uncertain object categories. As a result of this reasoning, the generator produces fewer nouns and names of distractor categories as compared to a literal speaker. We show that this conversational strategy for dealing with novel objects often improves communicative success, in terms of resolution accuracy of an automatic listener.
End-to-end Deep Reinforcement Learning Based Coreference Resolution
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, Florence, Italy, July 2019)

Abstract: Recent neural network models have significantly advanced the task of coreference resolution. However, current neural coreference models are usually trained with heuristic loss functions that are computed over a sequence of local decisions. In this paper, we introduce an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics. Specifically, we modify the state-of-the-art higher-order mention ranking approach in Lee et al. (2018) to a reinforced policy gradient model by incorporating the reward associated with a sequence of coreference linking actions. Furthermore, we introduce maximum entropy regularization for adequate exploration to prevent the model from prematurely converging to a bad local optimum. Our proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark.