Search Results for author: Min-Yen Kan

Found 72 papers, 27 papers with code

Re-examining the Role of Schema Linking in Text-to-SQL

no code implementations EMNLP 2020 Wenqiang Lei, Weixin Wang, Zhixin Ma, Tian Gan, Wei Lu, Min-Yen Kan, Tat-Seng Chua

By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking.


GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding

1 code implementation ACL 2022 Libo Qin, Qiguang Chen, Tianbao Xie, Qixin Li, Jian-Guang Lou, Wanxiang Che, Min-Yen Kan

Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance, then encourage their representations to be more similar than negative example pairs, which achieves to explicitly align representations of similar sentences across languages.

Contrastive Learning Cross-Lingual Transfer +1

SciWING– A Software Toolkit for Scientific Document Processing

no code implementations EMNLP (sdp) 2020 Abhinav Ramesh Kashyap, Min-Yen Kan

We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification.

Citation Intent Classification intent-classification +2

Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge

1 code implementation3 Jan 2023 Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables.

Semantic Parsing Text-To-Sql

MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences

1 code implementation23 Oct 2022 Wei Han, Hui Chen, Min-Yen Kan, Soujanya Poria

Existing multimodal tasks mostly target at the complete input modality setting, i. e., each modality is either complete or completely missing in both training and test sets.

Denoising Imputation

CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations

no code implementations COLING 2022 Lin Xu, Qixian Zhou, Jinlan Fu, Min-Yen Kan, See-Kiong Ng

Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally.


Modeling and Leveraging Prerequisite Context in Recommendation

no code implementations23 Sep 2022 Hengchang Hu, Liangming Pan, Yiding Ran, Min-Yen Kan

Prerequisites can play a crucial role in users' decision-making yet recommendation systems have not fully utilized such contextual background knowledge.

Decision Making Recommendation Systems

Comparative Snippet Generation

1 code implementation ECNLP (ACL) 2022 Saurabh Jain, Yisong Miao, Min-Yen Kan

We model product reviews to generate comparative responses consisting of positive and negative experiences regarding the product.

TraVLR: Now You See It, Now You Don't! Evaluating Cross-Modal Transfer of Visio-Linguistic Reasoning

no code implementations21 Nov 2021 Keng Ji Chow, Samson Tan, Min-Yen Kan

To address this issue and enable the evaluation of cross-modal transfer, we present TraVLR, a synthetic dataset comprising four V+L reasoning tasks.

Representation Learning

ContraQA: Question Answering under Contradicting Contexts

no code implementations15 Oct 2021 Liangming Pan, Wenhu Chen, Min-Yen Kan, William Yang Wang

With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over contradicting information to derive correct answers.

Misinformation Question Answering

Interpreting the Robustness of Neural NLP Models to Textual Perturbations

no code implementations Findings (ACL) 2022 Yunxiang Zhang, Liangming Pan, Samson Tan, Min-Yen Kan

In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence).

Data Augmentation

Zero-shot Fact Verification by Claim Generation

1 code implementation ACL 2021 Liangming Pan, Wenhu Chen, Wenhan Xiong, Min-Yen Kan, William Yang Wang

However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive.

Fact Verification

DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension

no code implementations26 Apr 2021 Jiaqi Li, Ming Liu, Zihao Zheng, Heng Zhang, Bing Qin, Min-Yen Kan, Ting Liu

Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC.

Machine Reading Comprehension Question Answering

N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking

no code implementations Findings (ACL) 2022 Taha Aksu, Zhengyuan Liu, Min-Yen Kan, Nancy F. Chen

Augmentation of task-oriented dialogues has followed standard methods used for plain-text such as back-translation, word-level manipulation, and paraphrasing despite its richly annotated structure.

Data Augmentation Dialogue State Tracking +3

Exploring Question-Specific Rewards for Generating Deep Questions

1 code implementation COLING 2020 Yuxi Xie, Liangming Pan, Dongzhe Wang, Min-Yen Kan, Yansong Feng

Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log-likelihood of ground-truth questions using teacher forcing.

Question Generation Question-Generation

Multi-modal Cooking Workflow Construction for Food Recipes

no code implementations20 Aug 2020 Liangming Pan, Jingjing Chen, Jianlong Wu, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Yu-Gang Jiang, Tat-Seng Chua

Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow of the recipe.

Common Sense Reasoning

FANG: Leveraging Social Context for Fake News Detection Using Graph Representation

1 code implementation18 Aug 2020 Van-Hoang Nguyen, Kazunari Sugiyama, Preslav Nakov, Min-Yen Kan

In particular, FANG yields significant improvements for the task of fake news detection, and it is robust in the case of limited training data.

Fake News Detection Representation Learning

The MUIR Framework: Cross-Linking MOOC Resources to Enhance Discussion Forums

no code implementations15 May 2020 Ya-Hui An, Muthu Kumar Chandresekaran, Min-Yen Kan, Yan Fu

We demonstrate the feasibility of this approach to the automatic identification, linking and resolution -- a task known as Wikification -- of learning resources mentioned on MOOC discussion forums, from a harvested collection of 100K+ resources.

It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbations

1 code implementation ACL 2020 Samson Tan, Shafiq Joty, Min-Yen Kan, Richard Socher

Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e. g., African American Vernacular English, Colloquial Singapore English, etc.).

Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding

1 code implementation EMNLP 2020 Samson Tan, Shafiq Joty, Lav R. Varshney, Min-Yen Kan

Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English.

Morphological Inflection Translation

Semantic Graphs for Generating Deep Questions

1 code implementation ACL 2020 Liangming Pan, Yuxi Xie, Yansong Feng, Tat-Seng Chua, Min-Yen Kan

This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input passage.

Question Generation Question-Generation

SciWING -- A Software Toolkit for Scientific Document Processing

1 code implementation8 Apr 2020 Abhinav Ramesh Kashyap, Min-Yen Kan

We introduce SciWING, an open-source software toolkit which provides access to pre-trained models for scientific document processing tasks, inclusive of citation string parsing and logical structure recovery.

Transfer Learning

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems

no code implementations21 Feb 2020 Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat-Seng Chua

Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models.

Recommendation Systems

A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages

no code implementations19 Nov 2019 Minh-Thang Luong, Preslav Nakov, Min-Yen Kan

We propose a language-independent approach for improving statistical machine translation for morphologically rich languages using a hybrid morpheme-word representation where the basic unit of translation is the morpheme, but word boundaries are respected at all stages of the translation process.

Machine Translation Translation

Sentiment Aware Neural Machine Translation

no code implementations WS 2019 Chenglei Si, Kui Wu, Ai Ti Aw, Min-Yen Kan

We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods.

Machine Translation NMT +2

What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?

no code implementations28 Oct 2019 Chenglei Si, Shuohang Wang, Min-Yen Kan, Jing Jiang

Based on our experiments on the 5 key MCRC datasets - RACE, MCTest, MCScript, MCScript2. 0, DREAM - we observe that 1) fine-tuned BERT mainly learns how keywords lead to correct prediction, instead of learning semantic understanding and reasoning; and 2) BERT does not need correct syntactic information to solve the task; 3) there exists artifacts in these datasets such that they can be solved even without the full context.

Multiple-choice Reading Comprehension

The CL-SciSumm Shared Task 2018: Results and Key Insights

1 code implementation2 Sep 2019 Kokil Jaidka, Michihiro Yasunaga, Muthu Kumar Chandrasekaran, Dragomir Radev, Min-Yen Kan

This overview describes the official results of the CL-SciSumm Shared Task 2018 -- the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain.

Document Summarization Information Retrieval +2

Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction

no code implementations NAACL 2019 Animesh Prasad, Min-Yen Kan

Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures.

Keyphrase Extraction

When to reply? Context Sensitive Models to Predict Instructor Interventions in MOOC Forums

1 code implementation26 May 2019 Muthu Kumar Chandrasekaran, Min-Yen Kan

We propose novel attention based models to infer the amount of latent context necessary to predict instructor intervention.

Recent Advances in Neural Question Generation

no code implementations22 May 2019 Liangming Pan, Wenqiang Lei, Tat-Seng Chua, Min-Yen Kan

Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition.

Question Generation Question-Generation

Resource Mention Extraction for MOOC Discussion Forums

no code implementations21 Nov 2018 Ya-Hui An, Liangming Pan, Min-Yen Kan, Qiang Dong, Yan Fu

We propose the novel problem of learning resource mention identification in MOOC forums.

Treatment Side Effect Prediction from Online User-generated Content

no code implementations WS 2018 Van Hoang Nguyen, Kazunari Sugiyama, Min-Yen Kan, Kishaloy Halder

With Health 2. 0, patients and caregivers increasingly seek information regarding possible drug side effects during their medical treatments in online health communities.

Feature Engineering

Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach

no code implementations WS 2017 Kishaloy Halder, Lahari Poddar, Min-Yen Kan

We study the problem of predicting a patient{'}s emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it.

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

3 code implementations16 Aug 2017 Xiangnan He, Hanwang Zhang, Min-Yen Kan, Tat-Seng Chua

To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data.

BiRank: Towards Ranking on Bipartite Graphs

3 code implementations15 Aug 2017 Xiangnan He, Ming Gao, Min-Yen Kan, Dingxian Wang

In this paper, we study the problem of ranking vertices of a bipartite graph, based on the graph's link structure as well as prior information about vertices (which we term a query vector).

WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling

no code implementations SEMEVAL 2017 Animesh Prasad, Min-Yen Kan

We describe an end-to-end pipeline processing approach for SemEval 2017{'}s Task 10 to extract keyphrases and their relations from scientific publications.

General Classification Keyphrase Extraction

Using Discourse Signals for Robust Instructor Intervention Prediction

1 code implementation3 Dec 2016 Muthu Kumar Chandrasekaran, Carrie Demmans Epp, Min-Yen Kan, Diane Litman

We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs).

A Comparison of Word Embeddings for English and Cross-Lingual Chinese Word Sense Disambiguation

no code implementations WS 2016 Hong Jin Kang, Tao Chen, Muthu Kumar Chandrasekaran, Min-Yen Kan

Thus we have also applied word embeddings to the novel task of cross-lingual WSD for Chinese and provide a public dataset for further benchmarking.

Translation Word Embeddings +1

QANUS: An Open-source Question-Answering Platform

no code implementations1 Jan 2015 Jun-Ping Ng, Min-Yen Kan

In this paper, we motivate the need for a publicly available, generic software framework for question-answering (QA) systems.

Question Answering

Linear Segmentation and Segment Significance

1 code implementation15 Sep 1998 Min-Yen Kan, Judith L. Klavans, Kathleen R. McKeown

We present a new method for discovering a segmental discourse structure of a document while categorizing segment function.


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