Haimin Zhang


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On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles
Rakesh Gosangi | Ravneet Arora | Mohsen Gheisarieha | Debanjan Mahata | Haimin Zhang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles. We formulate this problem as a sequence labeling task solved using a hierarchical BiLSTM model. We contribute a new benchmark dataset containing over two million sentences and their corresponding labels. We preserve the sentence order in this dataset and perform document-level train/test splits, which importantly allows incorporating contextual information in the modeling process. We evaluate the proposed approach on three benchmark datasets. Our results quantify the benefits of using context and contextual embeddings for citation worthiness. Lastly, through error analysis, we provide insights into cases where context plays an essential role in predicting citation worthiness.


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An Annotated Dataset of Discourse Modes in Hindi Stories
Swapnil Dhanwal | Hritwik Dutta | Hitesh Nankani | Nilay Shrivastava | Yaman Kumar | Junyi Jessy Li | Debanjan Mahata | Rakesh Gosangi | Haimin Zhang | Rajiv Ratn Shah | Amanda Stent
Proceedings of the 12th Language Resources and Evaluation Conference

In this paper, we present a new corpus consisting of sentences from Hindi short stories annotated for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. We present a detailed account of the entire data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.87 k-alpha). We analyze the data in terms of label distributions, part of speech tags, and sentence lengths. We characterize the performance of various classification algorithms on this dataset and perform ablation studies to understand the nature of the linguistic models suitable for capturing the nuances of the embedded discourse structures in the presented corpus.

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A Preliminary Exploration of GANs for Keyphrase Generation
Avinash Swaminathan | Haimin Zhang | Debanjan Mahata | Rakesh Gosangi | Rajiv Ratn Shah | Amanda Stent
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We introduce a new keyphrase generation approach using Generative Adversarial Networks (GANs). For a given document, the generator produces a sequence of keyphrases, and the discriminator distinguishes between human-curated and machine-generated keyphrases. We evaluated this approach on standard benchmark datasets. We observed that our model achieves state-of-the-art performance in the generation of abstractive keyphrases and is comparable to the best performing extractive techniques. Although we achieve promising results using GANs, they are not significantly better than the state-of-the-art generative models. To our knowledge, this is one of the first works that use GANs for keyphrase generation. We present a detailed analysis of our observations and expect that these findings would help other researchers to further study the use of GANs for the task of keyphrase generation.

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MIDAS at SemEval-2020 Task 10: Emphasis Selection Using Label Distribution Learning and Contextual Embeddings
Sarthak Anand | Pradyumna Gupta | Hemant Yadav | Debanjan Mahata | Rakesh Gosangi | Haimin Zhang | Rajiv Ratn Shah
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper presents our submission to the SemEval 2020 - Task 10 on emphasis selection in written text. We approach this emphasis selection problem as a sequence labeling task where we represent the underlying text with various contextual embedding models. We also employ label distribution learning to account for annotator disagreements. We experiment with the choice of model architectures, trainability of layers, and different contextual embeddings. Our best performing architecture is an ensemble of different models, which achieved an overall matching score of 0.783, placing us 15th out of 31 participating teams. Lastly, we analyze the results in terms of parts of speech tags, sentence lengths, and word ordering.

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Two-Step Classification using Recasted Data for Low Resource Settings
Shagun Uppal | Vivek Gupta | Avinash Swaminathan | Haimin Zhang | Debanjan Mahata | Rakesh Gosangi | Rajiv Ratn Shah | Amanda Stent
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

An NLP model’s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.


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MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter
Debanjan Mahata | Sarthak Anand | Haimin Zhang | Simra Shahid | Laiba Mehnaz | Yaman Kumar | Rajiv Ratn Shah
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

In this paper, we present our approach and the system description for the Social Media Mining for Health Applications (SMM4H) Shared Task 1,2 and 4 (2019). Our main contribution is to show the effectiveness of Transfer Learning approaches like BERT and ULMFiT, and how they generalize for the classification tasks like identification of adverse drug reaction mentions and reporting of personal health problems in tweets. We show the use of stacked embeddings combined with BLSTM+CRF tagger for identifying spans mentioning adverse drug reactions in tweets. We also show that these approaches perform well even with imbalanced dataset in comparison to undersampling and oversampling.

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MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter
Debanjan Mahata | Haimin Zhang | Karan Uppal | Yaman Kumar | Rajiv Ratn Shah | Simra Shahid | Laiba Mehnaz | Sarthak Anand
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper we present our approach and the system description for Sub Task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media. Sub Task A involves identifying if a given tweet is offensive and Sub Task B involves detecting if an offensive tweet is targeted towards someone (group or an individual). Our models for Sub Task A is based on an ensemble of Convolutional Neural Network and Bidirectional LSTM, whereas for Sub Task B, we rely on a set of heuristics derived from the training data. We provide detailed analysis of the results obtained using the trained models. Our team ranked 5th out of 103 participants in Sub Task A, achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants achieving a macro F1 of 0.695.

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MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit
Sarthak Anand | Debanjan Mahata | Kartik Aggarwal | Laiba Mehnaz | Simra Shahid | Haimin Zhang | Yaman Kumar | Rajiv Shah | Karan Uppal
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper we present our approach to tackle the Suggestion Mining from Online Reviews and Forums Sub-Task A. Given a review, we are asked to predict whether the review consists of a suggestion or not. Our model is based on Universal Language Model Fine-tuning for Text Classification. We apply various pre-processing techniques before training the language and the classification model. We further provide analysis of the model. Our team ranked 10th out of 34 participants, achieving an F1 score of 0.7011.