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1. Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification

   https://doi.org/10.18653/v1/2020.acl-main.512  

   Ma, Nianzu; Mazumder, Sahisnu; Wang, Hao; Liu, Bing (July 2020, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics)

   This paper studies the task of comparative preference classification (CPC). Given two entities in a sentence, our goal is to classify whether the first (or the second) entity is preferred over the other or no comparison is expressed at all between the two entities. Existing works either do not learn entity-aware representations well and fail to deal with sentences involving multiple entity pairs or use sequential modeling approaches that are unable to capture long-range dependencies between the entities. Some also use traditional machine learning approaches that do not generalize well. This paper proposes a novel Entity-aware Dependency-based Deep Graph Attention Network (ED-GAT) that employs a multi-hop graph attention over a dependency graph sentence representation to leverage both the semantic information from word embeddings and the syntactic information from the dependency graph to solve the problem. Empirical evaluation shows that the proposed model achieves the state-of-the-art performance in comparative preference classification. 

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2. Quantifying syntax similarity with a polynomial representation of dependency trees

   https://doi.org/10.53482/2022_53_402  

   Liu, Pengyu; Feng, Tinghao; Liu, Rui (January 2022, Glottometrics)

   We introduce a graph polynomial that distinguishes tree structures to represent dependency grammar and a measure based on the polynomial representation to quantify syntax similarity. The polynomial encodes accurate and comprehensive information about the dependency structure and dependency relations of words in a sentence, which enables in-depth analysis of dependency trees with data analysis tools. We apply the polynomial-based methods to analyze sentences in the ParallelUniversal Dependencies treebanks. Specifically, we compare the syntax of sentences and their translations in different languages, and we perform a syntactic typology study of available languages in the Parallel Universal Dependencies treebanks. We also demonstrate and discuss the potential of the methods in measuring syntax diversity of corpora. 

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3. Electrophysiological hallmarks for event relations and event roles in working memory

   https://doi.org/10.3389/fnins.2023.1282869  

   Yu, Xinchi; Li, Jialu; Zhu, Hao; Tian, Xing; Lau, Ellen (January 2024, Frontiers in Neuroscience)

   The ability to maintain events (i.e., interactions between/among objects) in working memory is crucial for our everyday cognition, yet the format of this representation is poorly understood. The current ERP study was designed to answer two questions: How is maintaining events (e.g., the tiger hit the lion) neurally different from maintaining item coordinations (e.g., the tiger and the lion)? That is, how is the event relation (present in events but not coordinations) represented? And how is the agent, or initiator of the event encoded differently from the patient, or receiver of the event during maintenance? We used a novel picture-sentence match-across-delay approach in which the working memory representation was “pinged” during the delay, replicated across two ERP experiments with Chinese and English materials. We found that maintenance of events elicited a long-lasting late sustained difference in posterior-occipital electrodes relative to non-events. This effect resembled the negative slow wave reported in previous studies of working memory, suggesting that the maintenance of events in working memory may impose a higher cost compared to coordinations. Although we did not observe significant ERP differences associated with pinging the agent vs. the patient during the delay, we did find that the ping appeared to dampen the ongoing sustained difference, suggesting a shift from sustained activity to activity silent mechanisms. These results suggest a new method by which ERPs can be used to elucidate the format of neural representation for events in working memory. 

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4. Exploiting the Syntax-Model Consistency for Neural Relation Extraction

   https://doi.org/10.18653/v1/2020.acl-main.715  

   Pouran Ben Veyseh, Amir; Dernoncourt, Franck; Dou, Dejing; Nguyen, Thien Huu (July 2020, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics)

   null (Ed.)

   This paper studies the task of Relation Extraction (RE) that aims to identify the semantic relations between two entity mentions in text. In the deep learning models for RE, it has been beneficial to incorporate the syntactic structures from the dependency trees of the input sentences. In such models, the dependency trees are often used to directly structure the network architectures or to obtain the dependency relations between the word pairs to inject the syntactic information into the models via multi-task learning. The major problem with these approaches is the lack of generalization beyond the syntactic structures in the training data or the failure to capture the syntactic importance of the words for RE. In order to overcome these issues, we propose a novel deep learning model for RE that uses the dependency trees to extract the syntax-based importance scores for the words, serving as a tree representation to introduce syntactic information into the models with greater generalization. In particular, we leverage Ordered-Neuron Long-Short Term Memory Networks (ON-LSTM) to infer the model-based importance scores for RE for every word in the sentences that are then regulated to be consistent with the syntax-based scores to enable syntactic information injection. We perform extensive experiments to demonstrate the effectiveness of the proposed method, leading to the state-of-the-art performance on three RE benchmark datasets. 

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5. Jointly Parse and Fragment Ungrammatical Sentences

   Hashemi, Homa B; Hwa, Rebecca (February 2018, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence)

   This paper is about detecting incorrect arcs in a dependency parse for sentences that contain grammar mistakes. Pruning these arcs results in well-formed parse fragments that can still be useful for downstream applications. We propose two automatic methods that jointly parse the ungrammatical sentence and prune the incorrect arcs: a parser retrained on a parallel corpus of ungrammatical sentences with their corrections, and a sequence-to sequence method. Experimental results show that the proposed strategies are promising for detecting incorrect syntactic dependencies as well as incorrect semantic dependencies. 

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