Search Results for author: Yufang Hou

Found 39 papers, 13 papers with code

Constrained Multi-Task Learning for Bridging Resolution

1 code implementation ACL 2022 Hideo Kobayashi, Yufang Hou, Vincent Ng

We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-train the entity coreference model in the multi-task framework on the large amount of publicly available coreference data; and (3) integrating prior knowledge encoded in rule-based resolvers.

Multi-Task Learning

Finding Sub-task Structure with Natural Language Instruction

no code implementations LNLS (ACL) 2022 Ryokan Ri, Yufang Hou, Radu Marinescu, Akihiro Kishimoto

When mapping a natural language instruction to a sequence of actions, it is often useful toidentify sub-tasks in the instruction.

Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead

no code implementations NAACL (sdp) 2021 Khalid Al Khatib, Tirthankar Ghosal, Yufang Hou, Anita de Waard, Dayne Freitag

Argument mining targets structures in natural language related to interpretation and persuasion which are central to scientific communication.

Argument Mining

Overview of the 2021 Key Point Analysis Shared Task

no code implementations EMNLP (ArgMining) 2021 Roni Friedman, Lena Dankin, Yufang Hou, Ranit Aharonov, Yoav Katz, Noam Slonim

We describe the 2021 Key Point Analysis (KPA-2021) shared task on key point analysis that we organized as a part of the 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP 2021.

Argument Mining Text Summarization

End-to-end Neural Information Status Classification

1 code implementation Findings (EMNLP) 2021 Yufang Hou

In this paper, we propose an end-to-end neural approach for information status classification.

Classification

Employing Argumentation Knowledge Graphs for Neural Argument Generation

1 code implementation ACL 2021 Khalid Al Khatib, Lukas Trautner, Henning Wachsmuth, Yufang Hou, Benno Stein

Generating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines.

Knowledge Graphs Text Generation

End-to-End NLP Knowledge Graph Construction

no code implementations2 Jun 2021 Ishani Mondal, Yufang Hou, Charles Jochim

This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers.

graph construction

D2S: Document-to-Slide Generation Via Query-Based Text Summarization

1 code implementation NAACL 2021 Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy X. R. Wang

Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming.

Question Answering Text Summarization

Fine-grained Information Status Classification Using Discourse Context-Aware BERT

1 code implementation COLING 2020 Yufang Hou

Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS).

General Classification

HBCP Corpus: A New Resource for the Analysis of Behavioural Change Intervention Reports

no code implementations LREC 2020 Francesca Bonin, Martin Gleize, Ailbhe Finnerty, C. Moore, ice, Charles Jochim, Emma Norris, Yufang Hou, Alison J. Wright, Debasis Ganguly, Emily Hayes, Silje Zink, Aless Pascale, ra, Pol Mac Aonghusa, Susan Michie

Due to the fast pace at which research reports in behaviour change are published, researchers, consultants and policymakers would benefit from more automatic ways to process these reports.

Bridging Anaphora Resolution as Question Answering

1 code implementation ACL 2020 Yufang Hou

Most previous studies on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b; Hou, 2018a) use the pairwise model to tackle the problem and assume that the gold mention information is given.

Bridging Anaphora Resolution Question Answering +1

Fine-grained Information Status Classification Using Discourse Context-Aware Self-Attention

no code implementations13 Aug 2019 Yufang Hou

Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS).

General Classification

Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction

1 code implementation ACL 2019 Yufang Hou, Charles Jochim, Martin Gleize, Francesca Bonin, Debasis Ganguly

While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult.

Scientific Results Extraction

A Deterministic Algorithm for Bridging Anaphora Resolution

no code implementations EMNLP 2018 Yufang Hou

Additionally, we further improve the results for bridging anaphora resolution reported in Hou (2018) by combining our simple deterministic approach with Hou et al.(2013b)'s best system MLN II.

Bridging Anaphora Resolution Word Embeddings

Unrestricted Bridging Resolution

no code implementations CL 2018 Yufang Hou, Katja Markert, Michael Strube

The second stage, bridging antecedent selection, finds the antecedents for all predicted bridging anaphors.

General Classification

Argument Relation Classification Using a Joint Inference Model

no code implementations WS 2017 Yufang Hou, Charles Jochim

In this paper, we address the problem of argument relation classification where argument units are from different texts.

Argument Mining Classification +3

Computational Argumentation Quality Assessment in Natural Language

no code implementations EACL 2017 Henning Wachsmuth, Nona Naderi, Yufang Hou, Yonatan Bilu, Vinodkumar Prabhakaran, Tim Alberdingk Thijm, Graeme Hirst, Benno Stein

Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation.

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