Lingjia Deng


2021

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ERNIE-NLI: Analyzing the Impact of Domain-Specific External Knowledge on Enhanced Representations for NLI
Lisa Bauer | Lingjia Deng | Mohit Bansal
Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures

We examine the effect of domain-specific external knowledge variations on deep large scale language model performance. Recent work in enhancing BERT with external knowledge has been very popular, resulting in models such as ERNIE (Zhang et al., 2019a). Using the ERNIE architecture, we provide a detailed analysis on the types of knowledge that result in a performance increase on the Natural Language Inference (NLI) task, specifically on the Multi-Genre Natural Language Inference Corpus (MNLI). While ERNIE uses general TransE embeddings, we instead train domain-specific knowledge embeddings and insert this knowledge via an information fusion layer in the ERNIE architecture, allowing us to directly control and analyze knowledge input. Using several different knowledge training objectives, sources of knowledge, and knowledge ablations, we find a strong correlation between knowledge and classification labels within the same polarity, illustrating that knowledge polarity is an important feature in predicting entailment. We also perform classification change analysis across different knowledge variations to illustrate the importance of selecting appropriate knowledge input regarding content and polarity, and show representative examples of these changes.

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Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification
Alexander Spangher | Jonathan May | Sz-Rung Shiang | Lingjia Deng
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

As labeling schemas evolve over time, small differences can render datasets following older schemas unusable. This prevents researchers from building on top of previous annotation work and results in the existence, in discourse learning in particular, of many small class-imbalanced datasets. In this work, we show that a multitask learning approach can combine discourse datasets from similar and diverse domains to improve discourse classification. We show an improvement of 4.9% Micro F1-score over current state-of-the-art benchmarks on the NewsDiscourse dataset, one of the largest discourse datasets recently published, due in part to label correlations across tasks, which improve performance for underrepresented classes. We also offer an extensive review of additional techniques proposed to address resource-poor problems in NLP, and show that none of these approaches can improve classification accuracy in our setting.

2016

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How can NLP Tasks Mutually Benefit Sentiment Analysis? A Holistic Approach to Sentiment Analysis
Lingjia Deng | Janyce Wiebe
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2015

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Joint Prediction for Entity/Event-Level Sentiment Analysis using Probabilistic Soft Logic Models
Lingjia Deng | Janyce Wiebe
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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MPQA 3.0: An Entity/Event-Level Sentiment Corpus
Lingjia Deng | Janyce Wiebe
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Entity/Event-Level Sentiment Detection and Inference
Lingjia Deng
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

2014

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Sentiment Propagation via Implicature Constraints
Lingjia Deng | Janyce Wiebe
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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An Investigation for Implicatures in Chinese : Implicatures in Chinese and in English are similar !
Lingjia Deng | Janyce Wiebe
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Lexical Acquisition for Opinion Inference: A Sense-Level Lexicon of Benefactive and Malefactive Events
Yoonjung Choi | Lingjia Deng | Janyce Wiebe
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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A Conceptual Framework for Inferring Implicatures
Janyce Wiebe | Lingjia Deng
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Joint Inference and Disambiguation of Implicit Sentiments via Implicature Constraints
Lingjia Deng | Janyce Wiebe | Yoonjung Choi
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Benefactive/Malefactive Event and Writer Attitude Annotation
Lingjia Deng | Yoonjung Choi | Janyce Wiebe
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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CPN-CORE: A Text Semantic Similarity System Infused with Opinion Knowledge
Carmen Banea | Yoonjung Choi | Lingjia Deng | Samer Hassan | Michael Mohler | Bishan Yang | Claire Cardie | Rada Mihalcea | Jan Wiebe
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity