Lidan Wang


pdf bib
Open-Domain Question Answering with Pre-Constructed Question Spaces
Jinfeng Xiao | Lidan Wang | Franck Dernoncourt | Trung Bui | Tong Sun | Jiawei Han
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

Open-domain question answering aims at locating the answers to user-generated questions in massive collections of documents. Retriever-readers and knowledge graph approaches are two big families of solutions to this task. A retriever-reader first applies information retrieval techniques to locate a few passages that are likely to be relevant, and then feeds the retrieved text to a neural network reader to extract the answer. Alternatively, knowledge graphs can be constructed and queried to answer users’ questions. We propose an algorithm with a novel reader-retriever design that differs from both families. Our reader-retriever first uses an offline reader to read the corpus and generate collections of all answerable questions associated with their answers, and then uses an online retriever to respond to user queries by searching the pre-constructed question spaces for answers that are most likely to be asked in the given way. We further combine one retriever-reader and two reader-retrievers into a hybrid model called R6 for the best performance. Experiments with two large-scale public datasets show that R6 achieves state-of-the-art accuracy.


pdf bib
Understanding Points of Correspondence between Sentences for Abstractive Summarization
Logan Lebanoff | John Muchovej | Franck Dernoncourt | Doo Soon Kim | Lidan Wang | Walter Chang | Fei Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Fusing sentences containing disparate content is a remarkable human ability that helps create informative and succinct summaries. Such a simple task for humans has remained challenging for modern abstractive summarizers, substantially restricting their applicability in real-world scenarios. In this paper, we present an investigation into fusing sentences drawn from a document by introducing the notion of points of correspondence, which are cohesive devices that tie any two sentences together into a coherent text. The types of points of correspondence are delineated by text cohesion theory, covering pronominal and nominal referencing, repetition and beyond. We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences. Our dataset bridges the gap between coreference resolution and summarization. It is publicly shared to serve as a basis for future work to measure the success of sentence fusion systems.

pdf bib
Learning to Fuse Sentences with Transformers for Summarization
Logan Lebanoff | Franck Dernoncourt | Doo Soon Kim | Lidan Wang | Walter Chang | Fei Liu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts. However, to date, summarizers can fail on fusing sentences. They tend to produce few summary sentences by fusion or generate incorrect fusions that lead the summary to fail to retain the original meaning. In this paper, we explore the ability of Transformers to fuse sentences and propose novel algorithms to enhance their ability to perform sentence fusion by leveraging the knowledge of points of correspondence between sentences. Through extensive experiments, we investigate the effects of different design choices on Transformer’s performance. Our findings highlight the importance of modeling points of correspondence between sentences for effective sentence fusion.


pdf bib
Margin Call: an Accessible Web-based Text Viewer with Generated Paragraph Summaries in the Margin
Naba Rizvi | Sebastian Gehrmann | Lidan Wang | Franck Dernoncourt
Proceedings of the 12th International Conference on Natural Language Generation

We present Margin Call, a web-based text viewer that automatically generates short summaries for each paragraph of the text and displays the summaries in the margin of the text next to the corresponding paragraph. On the back-end, the summarizer first identifies the most important sentence for each paragraph in the text file uploaded by the user. The selected sentence is then automatically compressed to produce the short summary. The resulting summary is a few words long. The displayed summaries can help the user understand and retrieve information faster from the text, while increasing the retention of information.


pdf bib
FastHybrid: A Hybrid Model for Efficient Answer Selection
Lidan Wang | Ming Tan | Jiawei Han
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Answer selection is a core component in any question-answering systems. It aims to select correct answer sentences for a given question from a pool of candidate sentences. In recent years, many deep learning methods have been proposed and shown excellent results for this task. However, these methods typically require extensive parameter (and hyper-parameter) tuning, which give rise to efficiency issues for large-scale datasets, and potentially make them less portable across new datasets and domains (as re-tuning is usually required). In this paper, we propose an extremely efficient hybrid model (FastHybrid) that tackles the problem from both an accuracy and scalability point of view. FastHybrid is a light-weight model that requires little tuning and adaptation across different domains. It combines a fast deep model (which will be introduced in the method section) with an initial information retrieval model to effectively and efficiently handle answer selection. We introduce a new efficient attention mechanism in the hybrid model and demonstrate its effectiveness on several QA datasets. Experimental results show that although the hybrid uses no training data, its accuracy is often on-par with supervised deep learning techniques, while significantly reducing training and tuning costs across different domains.


pdf bib
Efficient Hyper-parameter Optimization for NLP Applications
Lidan Wang | Minwei Feng | Bowen Zhou | Bing Xiang | Sridhar Mahadevan
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


pdf bib
Context-based Message Expansion for Disentanglement of Interleaved Text Conversations
Lidan Wang | Douglas W. Oard
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics