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1. Multi-View Consistency for Relation Extraction via Mutual Information and Structure Prediction

   https://doi.org/10.1609/aaai.v34i05.6445  

   Veyseh, Amir; Dernoncourt, Franck; Thai, My; Dou, Dejing; Nguyen, Thien (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Relation Extraction (RE) is one of the fundamental tasks in Information Extraction. The goal of this task is to find the semantic relations between entity mentions in text. It has been shown in many previous work that the structure of the sentences (i.e., dependency trees) can provide important information/features for the RE models. However, the common limitation of the previous work on RE is the reliance on some external parsers to obtain the syntactic trees for the sentence structures. On the one hand, it is not guaranteed that the independent external parsers can offer the optimal sentence structures for RE and the customized structures for RE might help to further improve the performance. On the other hand, the quality of the external parsers might suffer when applied to different domains, thus also affecting the performance of the RE models on such domains. In order to overcome this issue, we introduce a novel method for RE that simultaneously induces the structures and predicts the relations for the input sentences, thus avoiding the external parsers and potentially leading to better sentence structures for RE. Our general strategy to learn the RE-specific structures is to apply two different methods to infer the structures for the input sentences (i.e., two views). We then introduce several mechanisms to encourage the structure and semantic consistencies between these two views so the effective structure and semantic representations for RE can emerge. We perform extensive experiments on the ACE 2005 and SemEval 2010 datasets to demonstrate the advantages of the proposed method, leading to the state-of-the-art performance on such datasets. 

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2. Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language

   Zhang, S; Peng, H; Fu, J; Luo, J. (February 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   We address the problem of retrieving a specific moment from an untrimmed video by a query sentence. This is a challenging problem because a target moment may take place in relations to other temporal moments in the untrimmed video. Existing methods cannot tackle this challenge well since they consider temporal moments individually and neglect the temporal dependencies. In this paper, we model the temporal relations between video moments by a two-dimensional map, where one dimension indicates the starting time of a moment and the other indicates the end time. This 2D temporal map can cover diverse video moments with different lengths, while representing their adjacent relations. Based on the 2D map, we propose a Temporal Adjacent Network (2D-TAN), a single-shot framework for moment localization. It is capable of encoding the adjacent temporal relation, while learning discriminative features for matching video moments with referring expressions. We evaluate the proposed 2D-TAN on three challenging benchmarks, i.e., Charades-STA, ActivityNet Captions, and TACoS, where our 2D-TAN outperforms the state-of-the-art. 

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3. Effects of Feature Scaling and Fusion on Sign Language Translation

   https://doi.org/10.21437/Interspeech.2021-1863  

   Ananthanarayana, Tejaswini; Chaudhary, Lipisha; Nwogu, Ifeoma (August 2021, Proceedings of Interspeech 2021)

   Sign language translation without transcription has only recently started to gain attention. In our work, we focus on improving the state-of-the-art translation by introducing a multi-feature fusion architecture with enhanced input features. As sign language is challenging to segment, we obtain the input features by extracting overlapping scaled segments across the video and obtaining their 3D CNN representations. We exploit the attention mechanism in the fusion architecture by initially learning dependencies between different frames of the same video and later fusing them to learn the relations between different features from the same video. In addition to 3D CNN features, we also analyze pose-based features. Our robust methodology outperforms the state-of-the-art sign language translation model by achieving higher BLEU 3 – BLEU 4 scores and also outperforms the state-of-the-art sequence attention models by achieving a 43.54% increase in BLEU 4 score. We conclude that the combined effects of feature scaling and feature fusion make our model more robust in predicting longer n-grams which are crucial in continuous sign language translation. 

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4. Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning

   https://doi.org/10.18653/v1/2020.emnlp-main.719  

   Pouran Ben Veyseh, Amir; Nouri, Nasim; Dernoncourt, Franck; Dou, Dejing; Nguyen, Thien Huu (November 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   null (Ed.)

   Targeted opinion word extraction (TOWE) is a sub-task of aspect based sentiment analysis (ABSA) which aims to find the opinion words for a given aspect-term in a sentence. Despite their success for TOWE, the current deep learning models fail to exploit the syntactic information of the sentences that have been proved to be useful for TOWE in the prior research. In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words. We also introduce a novel regularization technique to improve the performance of the deep learning models based on the representation distinctions between the words in TOWE. The proposed model is extensively analyzed and achieves the state-of-the-art performance on four benchmark datasets. 

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5. Question-Answer Sentence Graph for Joint Modeling Answer Selection

   https://doi.org/10.18653/v1/2023.eacl-main.68  

   Iyer, Roshni; Vu, Thuy; Moschitti, Alessandro; Sun, Yizhou (May 2023, Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL))

   This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learning, our model constructs a small-scale relevant training graph per question in an unsupervised manner, and integrates with Graph Neural Networks. Graph nodes are question sentence to answer sentence pairs. We train and integrate state-of-the-art (SOTA) models for computing scores between question-question, question-answer, and answer-answer pairs, and use thresholding on relevance scores for creating graph edges. Online inference is then performed to solve the AS2 task on unseen queries. Experiments on two well-known academic benchmarks and a real-world dataset show that our approach consistently outperforms SOTA QA baseline models. 

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