Papers by Walter Chang
Rethinking Self-Attention: Towards Interpretability in Neural Parsing (2020.findings-emnlp)
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| Challenge: | Recent work shows that attention mechanisms provide arguably explainable attention distributions that can help to interpret predictions. |
| Approach: | They propose a new self-attention layer where attention heads represent labels. |
| Outcome: | The proposed model obtains state-of-the-art results on the Penn Treebank and Chinese Treebank. |
Multimodal Intent Discovery from Livestream Videos (2022.findings-naacl)
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Adyasha Maharana, Quan Tran, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Mohit Bansal
| Challenge: | Existing models for instructional video understanding struggle to understand abstract intents . identifying procedural intent within instructional videos is a challenging task . |
| Approach: | They propose to extract instructional intent from software instructional livestreams by using a multimodal cascaded cross-attention model that integrates weaker and noisier video signals with more discriminative text signals. |
| Outcome: | The proposed model improves on baseline models and compares it to existing models. |
Analyzing Sentence Fusion in Abstractive Summarization (D19-54)
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Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu
| Challenge: | Abstractive summarization systems struggle to combine information from multiple sources, resulting in poor grammar and incorrect facts. |
| Approach: | They analyze the outputs of five abstractive summarization systems and examine their grammatical accuracy and faithfulness. |
| Outcome: | The proposed summarization systems are able to combine information from multiple sources, but they often fail to remain faithful to the original document. |
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion (2020.aacl-main)
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| Challenge: | Existing systems that perform content selection and surface realization are not able to provide sufficient training data for news summarization. |
| Approach: | They propose to use a cascade architecture to perform content selection and surface realization together to generate abstracts. |
| Outcome: | The proposed architecture outperforms or outranks existing systems in terms of content selection and surface realization. |
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
| Challenge: | Existing abstractive summarization models focus on summarizing sentences and short documents. |
| Approach: | They propose a hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models on two large-scale datasets of scientific papers. |
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning (2021.emnlp-main)
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Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu
| Challenge: | Existing methods address few-shot intent detection tasks from two perspectives: data augmentation and task-adaptive training with pre-trained models. |
| Approach: | They propose a few-shot intent detection schema using contrastive pre-training and fine-tuning. |
| Outcome: | The proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings. |
MadDog: A Web-based System for Acronym Identification and Disambiguation (2021.eacl-demos)
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| Challenge: | Acronyms and abbreviations are the short-form of longer phrases and are frequently used in writing but they can also present challenges for newcomers. |
| Approach: | They propose to develop a web-based acronym identification and disambiguation system which can process acronyms from various domains including scientific, biomedical, and general domains. |
| Outcome: | The proposed system can process acronyms from scientific, biomedical, and general domains. |
A Repository of Corpora for Summarization (L18-1)
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| Challenge: | Summarization corpora are numerous but fragmented, making it difficult to pinpoint corporata best suited for a given summarization task. |
| Approach: | They propose a repository containing corpora available to train and evaluate automatic summarization systems. |
| Outcome: | The proposed system is based on a repository of corpora available for summarization tasks. |
StreamHover: Livestream Transcript Summarization and Annotation (2021.emnlp-main)
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Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh, Fei Liu
| Challenge: | StreamHover is a framework for annotating and summarizing livestream transcripts . the problem is that there is n't enough annotated datasets to summarize livestreams based on the informal nature of spoken language . |
| Approach: | They propose a framework for annotating and summarizing livestream transcripts using a text preview. |
| Outcome: | The proposed model generalizes better and improves over strong baselines. |
Understanding Points of Correspondence between Sentences for Abstractive Summarization (2020.acl-srw)
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| Challenge: | Using points of correspondence, fusion systems are difficult for abstractive summarizers because of their complexity. |
| Approach: | They propose to model points of correspondence between disparate sentences by combining documents, source and fusion sentences, and human annotations of points of correspondance between sentences. |
| Outcome: | The proposed model bridges the gap between coreference resolution and summarization by using human annotations of points of correspondence between sentences. |
Scoring Sentence Singletons and Pairs for Abstractive Summarization (P19-1)
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Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu
| Challenge: | Existing methods for summarizing content from single sentences are inadequately understood. |
| Approach: | They propose to combine singletons and pairs to create a summarizing sentence . they use a dataset of human-written abstracts to examine human-writing methods . |
| Outcome: | The proposed framework is based on human-written abstracts from three large datasets. |
Scene Graph Modification Based on Natural Language Commands (2020.findings-emnlp)
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Xuanli He, Quan Hung Tran, Gholamreza Haffari, Walter Chang, Zhe Lin, Trung Bui, Franck Dernoncourt, Nhan Dam
| Challenge: | Numerous parsing methods have been developed for a single sentence, while a typical human-computer interaction session or conversation is not singleturn. |
| Approach: | They propose to modify an existing scene graph given a new user's command by using graph-based sparse transformer and cross attention information fusion to improve performance. |
| Outcome: | The proposed models outperform previous systems adapted from the machine translation and graph generation literature and contribute to the research community. |
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution (2021.naacl-main)
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| Challenge: | Existing methods for event coreference resolution use symbolic features, but they are noisy and contain errors. |
| Approach: | They propose a context-dependent gated module to adaptively control the information flows from the input symbolic features. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets: ACE 2005 and KBP 2016 . |
Learning to Fuse Sentences with Transformers for Summarization (2020.emnlp-main)
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| Challenge: | Abstractive summarization systems that fuse sentences are not rewarded for correctly fusing sentences. |
| Approach: | They propose to leverage the knowledge of points of correspondence between sentences to enhance their ability to fuse sentences. |
| Outcome: | The proposed algorithms improve the ability of the proposed summarization systems to fuse sentences and show that they can fuse sentences in a way that retains the original meaning. |
Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing (L18-1)
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Ramesh Manuvinakurike, Jacqueline Brixey, Trung Bui, Walter Chang, Doo Soon Kim, Ron Artstein, Kallirroi Georgila
| Challenge: | a corpus of image edit requests is elicited for real world images, and an annotation framework is developed . evaluators evaluate crowd-sourced annotation as a means of efficiently creating a sizable corpus at a reasonable cost. |
| Approach: | They propose a natural language interface for interacting with an image editing program . they propose an annotation framework for understanding natural language requests . |
| Outcome: | The proposed tool interprets image edit requests and maps them to actionable commands. |
A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding (2021.acl-long)
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Khalil Mrini, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Emilia Farcas, Ndapa Nakashole
| Challenge: | Existing methods for medical question understanding often fail to provide high recall in answer retrieval. |
| Approach: | They propose a multi-task learning method with data augmentation for medical question understanding that uses just one dataset to optimize for both tasks. |
| Outcome: | The proposed method outperforms existing MTL methods across 4 datasets of medical question pairs in ROUGE scores, RQE accuracy and human evaluation. |
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation (2020.emnlp-main)
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| Challenge: | Recent studies have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. |
| Approach: | They propose a Variational Hierarchical Dialog Autoencoder for modeling the complete aspects of goal-oriented dialogs using inter-connected latent variables and learns to generate coherent dialogs from the latent spaces. |
| Outcome: | The proposed model outperforms previous strong baselines on dialog response generation and user simulation tasks. |
A Web-based Framework for Collecting and Assessing Highlighted Sentences in a Document (C18-2)
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| Challenge: | Existing automated summarization techniques are not fully mature, authors say . existing frameworks focus on summarizing text, but highlight is not always accurate . |
| Approach: | They propose a web-based framework to efficiently and scalably crowdsource two tasks . they aim to collect highlight annotations and compare the performance of automated highlighting systems . |
| Outcome: | The proposed framework can crowdsource two tasks to identify key portions of a document that are the most important to a reader. |
PhotoshopQuiA: A Corpus of Non-Factoid Questions and Answers for Why-Question Answering (L18-1)
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| Challenge: | Community Question Answering web sites are used for non-factoid question answering . however, there is a scarcity of available datasets for this task . cnn.com's john m. sutter is releasing a dataset for why-QA . |
| Approach: | They propose a dataset of 2,854 why-question and answer(s) pairs related to Adobe Photoshop usage from five CQA web sites. |
| Outcome: | The new dataset is the first English dataset for Why-QA that focuses on a product . it can be used to build Why-Q systems, evaluate approaches and develop new models . |