Search Results for author: Min-Yen Kan

Found 102 papers, 52 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.

Text-To-SQL

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 +2

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

The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language Models

1 code implementation14 Jun 2024 Yan Liu, Yu Liu, Xiaokang Chen, Pin-Yu Chen, Daoguang Zan, Min-Yen Kan, Tsung-Yi Ho

As a result, previous debiasing methods mainly finetune or even pre-train language models on newly constructed anti-stereotypical datasets, which are high-cost.

Fairness Language Modelling

Decompose and Aggregate: A Step-by-Step Interpretable Evaluation Framework

no code implementations24 May 2024 Minzhi Li, Zhengyuan Liu, Shumin Deng, Shafiq Joty, Nancy F. Chen, Min-Yen Kan

The acceleration of Large Language Models (LLMs) research has opened up new possibilities for evaluating generated texts.

Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems

no code implementations3 May 2024 Chuang Li, Yang Deng, Hengchang Hu, Min-Yen Kan, Haizhou Li

This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks.

Informativeness Recommendation Systems

Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning

1 code implementation1 May 2024 Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P. Lillicrap, Kenji Kawaguchi, Michael Shieh

We introduce an approach aimed at enhancing the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process inspired by the successful strategy employed by AlphaZero.

GSM8K Math

ISQA: Informative Factuality Feedback for Scientific Summarization

1 code implementation20 Apr 2024 Zekai Li, Yanxia Qin, Qian Liu, Min-Yen Kan

We propose Iterative Facuality Refining on Informative Scientific Question-Answering (ISQA) feedback\footnote{Code is available at \url{https://github. com/lizekai-richard/isqa}}, a method following human learning theories that employs model-generated feedback consisting of both positive and negative information.

Question Answering

Discrete Semantic Tokenization for Deep CTR Prediction

2 code implementations13 Mar 2024 Qijiong Liu, Hengchang Hu, Jiahao Wu, Jieming Zhu, Min-Yen Kan, Xiao-Ming Wu

Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.

Click-Through Rate Prediction News Recommendation

Beyond Memorization: The Challenge of Random Memory Access in Language Models

1 code implementation12 Mar 2024 Tongyao Zhu, Qian Liu, Liang Pang, Zhengbao Jiang, Min-Yen Kan, Min Lin

Through carefully-designed synthetic tasks, covering the scenarios of full recitation, selective recitation and grounded question answering, we reveal that LMs manage to sequentially access their memory while encountering challenges in randomly accessing memorized content.

Memorization Open-Domain Question Answering

NNOSE: Nearest Neighbor Occupational Skill Extraction

1 code implementation30 Jan 2024 Mike Zhang, Rob van der Goot, Min-Yen Kan, Barbara Plank

The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text.

Retrieval

Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision

no code implementations14 Jan 2024 Hengchang Hu, Qijiong Liu, Chuang Li, Min-Yen Kan

Specifically, we introduce a novel method that enhances the learning of embeddings in SR through the supervision of modality correlations.

Knowledge Distillation Representation Learning +1

ChOiRe: Characterizing and Predicting Human Opinions with Chain of Opinion Reasoning

no code implementations14 Nov 2023 Xuan Long Do, Kenji Kawaguchi, Min-Yen Kan, Nancy F. Chen

Aligning language models (LMs) with human opinion is challenging yet vital to enhance their grasp of human values, preferences, and beliefs.

CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation

1 code implementation24 Oct 2023 Minzhi Li, Taiwei Shi, Caleb Ziems, Min-Yen Kan, Nancy F. Chen, Zhengyuan Liu, Diyi Yang

Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance.

text annotation

UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking

1 code implementation16 Oct 2023 Chuang Li, Yan Zhang, Min-Yen Kan, Haizhou Li

Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, ignoring unlabelled data in the target domain.

Dialogue State Tracking Transfer Learning

QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking

1 code implementation11 Oct 2023 Liangming Pan, Xinyuan Lu, Min-Yen Kan, Preslav Nakov

Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them.

Decision Making Fact Checking +1

Automatic Feature Fairness in Recommendation via Adversaries

1 code implementation27 Sep 2023 Hengchang Hu, Yiming Cao, Zhankui He, Samson Tan, Min-Yen Kan

We leverage the Adaptive Adversarial perturbation based on the widely-applied Factorization Machine (AAFM) as our backbone model.

Fairness Recommendation Systems

Investigating Zero- and Few-shot Generalization in Fact Verification

1 code implementation18 Sep 2023 Liangming Pan, Yunxiang Zhang, Min-Yen Kan

In this paper, we explore zero- and few-shot generalization for fact verification (FV), which aims to generalize the FV model trained on well-resourced domains (e. g., Wikipedia) to low-resourced domains that lack human annotations.

Fact Verification

A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems

1 code implementation14 Sep 2023 Chuang Li, Hengchang Hu, Yan Zhang, Min-Yen Kan, Haizhou Li

However, not all CRS approaches use human conversations as their source of interaction data; the majority of prior CRS work simulates interactions by exchanging entity-level information.

Language Modelling Recommendation Systems

FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation

1 code implementation10 Sep 2023 Yan Meng, Liangming Pan, Yixin Cao, Min-Yen Kan

We introduce the task of real-world information-seeking follow-up question generation (FQG), which aims to generate follow-up questions seeking a more in-depth understanding of an initial question and answer.

Informativeness Question Generation +1

Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems

1 code implementation30 Aug 2023 Hengchang Hu, Wei Guo, Yong liu, Min-Yen Kan

We propose a graph-based approach (named MMSR) to fuse modality features in an adaptive order, enabling each modality to prioritize either its inherent sequential nature or its interplay with other modalities.

Sequential Recommendation

Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization

no code implementations7 Aug 2023 Xiachong Feng, Xiaocheng Feng, Xiyuan Du, Min-Yen Kan, Bing Qin

However, existing work has focused on training models on centralized data, neglecting real-world scenarios where meeting data are infeasible to collect centrally, due to their sensitive nature.

Federated Learning Knowledge Distillation +1

Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation

1 code implementation7 Jun 2023 Taha Aksu, Min-Yen Kan, Nancy F. Chen

A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data, zero-shot domain adaptation.

Dialogue State Tracking Domain Adaptation +1

Songs Across Borders: Singable and Controllable Neural Lyric Translation

1 code implementation26 May 2023 Longshen Ou, Xichu Ma, Min-Yen Kan, Ye Wang

The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations.

Machine Translation NMT +1

ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning

1 code implementation24 May 2023 Yuxi Xie, Guanzhen Li, Min-Yen Kan

We introduce ECHo (Event Causality Inference via Human-Centric Reasoning), a diagnostic dataset of event causality inference grounded in visio-linguistic social scenarios.

The ACL OCL Corpus: Advancing Open Science in Computational Linguistics

no code implementations24 May 2023 Shaurya Rohatgi, Yanxia Qin, Benjamin Aw, Niranjana Unnithan, Min-Yen Kan

We present ACL OCL, a scholarly corpus derived from the ACL Anthology to assist Open scientific research in the Computational Linguistics domain.

Chunking Text Generation

On the Risk of Misinformation Pollution with Large Language Models

1 code implementation23 May 2023 Yikang Pan, Liangming Pan, Wenhu Chen, Preslav Nakov, Min-Yen Kan, William Yang Wang

In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems.

Misinformation Open-Domain Question Answering

Fact-Checking Complex Claims with Program-Guided Reasoning

1 code implementation22 May 2023 Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu, William Yang Wang, Min-Yen Kan, Preslav Nakov

Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning.

Fact Checking In-Context Learning

SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables

1 code implementation22 May 2023 Xinyuan Lu, Liangming Pan, Qian Liu, Preslav Nakov, Min-Yen Kan

Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence.

Claim Verification Fact Checking

Self-Evaluation Guided Beam Search for Reasoning

no code implementations NeurIPS 2023 Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, Xu Zhao, Min-Yen Kan, Junxian He, Qizhe Xie

Stochastic beam search balances exploitation and exploration of the search space with temperature-controlled randomness.

Arithmetic Reasoning GSM8K +3

Improving Recommendation Systems with User Personality Inferred from Product Reviews

no code implementations9 Mar 2023 Xinyuan Lu, Min-Yen Kan

Experiments on our two newly contributed personality datasets -- Amazon-beauty and Amazon-music -- validate our hypothesis, showing performance boosts of 3--28%. Our analysis uncovers that varying personality types contribute differently to recommendation performance: open and extroverted personalities are most helpful in music recommendation, while a conscientious personality is most helpful in beauty product recommendation.

Decision Making Music Recommendation +2

UDApter -- Efficient Domain Adaptation Using Adapters

1 code implementation7 Feb 2023 Bhavitvya Malik, Abhinav Ramesh Kashyap, Min-Yen Kan, Soujanya Poria

We even outperform unsupervised domain adaptation methods such as DANN and DSN in sentiment classification, and we are within 0. 85% F1 for natural language inference task, by fine-tuning only a fraction of the full model parameters.

Language Modelling Natural Language Inference +3

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.

Management

Modeling and Leveraging Prerequisite Context in Recommendation

1 code implementation23 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.

Sentence

TraVLR: Now You See It, Now You Don't! A Bimodal Dataset for Evaluating Visio-Linguistic Reasoning

1 code implementation21 Nov 2021 Keng Ji Chow, Samson Tan, Min-Yen Kan

Furthermore, existing V+L benchmarks often report global accuracy scores on the entire dataset, making it difficult to pinpoint the specific reasoning tasks that models fail and succeed at.

Representation Learning

Attacking Open-domain Question Answering by Injecting Misinformation

1 code implementation15 Oct 2021 Liangming Pan, Wenhu Chen, Min-Yen Kan, William Yang Wang

We curate both human-written and model-generated false documents that we inject into the evidence corpus of QA models and assess the impact on the performance of these systems.

Misinformation Open-Domain 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.

2k 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.

Graph Neural Network Machine Reading Comprehension +1

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 Decoder

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 Sentence +1

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 +3

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.

Clustering 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.

Clustering General Classification +2

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.

Benchmarking Translation +2

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.

Retrieval Segmentation

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