Papers by Haimin Zhang
On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles (2021.naacl-main)
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| Challenge: | citation worthiness is an emerging research topic in the natural language processing domain . citation recommendation systems are often approached as ranking problems . |
| Approach: | They propose a hierarchical biLSTM-based model that uses two adjacent sentences to solve a citation worthiness problem. |
| Outcome: | The proposed approach can be applied to a dataset of over two million sentences and their labels. |
A Preliminary Exploration of GANs for Keyphrase Generation (2020.emnlp-main)
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| Challenge: | Existing studies on extractive keyphrases have shown promising results, but the results suggest that there is room for improvement. |
| Approach: | They propose a new keyphrase generation approach using Generative Adversarial Networks (GANs) their model produces a sequence of keyphrases and a discriminator distinguishes between human-curated and machine-generated keyphrase. |
| Outcome: | The proposed model outperforms the state-of-the-art generative models on benchmark datasets and is comparable to the best performing extractive models. |
An Annotated Dataset of Discourse Modes in Hindi Stories (2020.lrec-1)
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Swapnil Dhanwal, Hritwik Dutta, Hitesh Nankani, Nilay Shrivastava, Yaman Kumar, Junyi Jessy Li, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah, Amanda Stent
| Challenge: | Using a new corpus of sentences from Hindi short stories, we analyze the annotations for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. |
| Approach: | They propose to annotate sentences from Hindi short stories for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. |
| Outcome: | The proposed corpus has a high inter-annotator agreement (0.87 k-alpha) and is able to capture the nuances of the embedded discourse structures. |
Two-Step Classification using Recasted Data for Low Resource Settings (2020.aacl-main)
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Shagun Uppal, Vivek Gupta, Avinash Swaminathan, Haimin Zhang, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah, Amanda Stent
| Challenge: | Existing studies on NLP models focus on high resource languages like English, but there are only two datasets for Hindi. |
| Approach: | They propose a novel two-step classification method which uses textual-entailment predictions for classification task. |
| Outcome: | The proposed method improves classification performance by using a joint-objective for classification and textual entailment. |