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Found 72 papers, 27 papers with code
Re-examining the Role of Schema Linking in Text-to-SQL
By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking.
Lightweight Contextual Logical Structure Recovery
Logical structure recovery in scientific articles associates text with a semantic section of the article.
GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
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.
SciWING– A Software Toolkit for Scientific Document Processing
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.
Analyzing the Domain Robustness of Pretrained Language Models, Layer by Layer
The robustness of pretrained language models(PLMs) is generally measured using performance drops on two or more domains.
Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synthetic Text Generation with Domain Adaptation
We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus availability problem for dialogue comprehension.
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
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.
MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences
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.
CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations
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
Prerequisites can play a crucial role in users' decision-making yet recommendation systems have not fully utilized such contextual background knowledge.
Comparative Snippet Generation
We model product reviews to generate comparative responses consisting of positive and negative experiences regarding the product.
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer
Automatic transfer of text between domains has become popular in recent times.
GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
We present Global--Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming.
TraVLR: Now You See It, Now You Don't! Evaluating Cross-Modal Transfer of Visio-Linguistic Reasoning
To address this issue and enable the evaluation of cross-modal transfer, we present TraVLR, a synthetic dataset comprising four V+L reasoning tasks.
ContraQA: Question Answering under Contradicting Contexts
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.
Interpreting the Robustness of Neural NLP Models to Textual Perturbations
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).
Zero-shot Fact Verification by Claim Generation
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.
Reliability Testing for Natural Language Processing Systems
Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems.
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension
Multiparty Dialogue Machine Reading Comprehension (MRC) differs from traditional MRC as models must handle the complex dialogue discourse structure, previously unconsidered in traditional MRC.
Ranked #4 on
Question Answering
on Molweni
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking
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.
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework
We introduce a deep learning model to learn the set of enumerated job skills associated with a job description.
Exploring Question-Specific Rewards for Generating Deep Questions
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.
Unsupervised Multi-hop Question Answering by Question Generation
Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive.
Domain Divergences: a Survey and Empirical Analysis
Domain divergence plays a significant role in estimating the performance of a model in new domains.
Multi-modal Cooking Workflow Construction for Food Recipes
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.
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
In particular, FANG yields significant improvements for the task of fake news detection, and it is robust in the case of limited training data.
The MUIR Framework: Cross-Linking MOOC Resources to Enhance Discussion Forums
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
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.).
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen
The curse of knowledge can impede communication between experts and laymen.
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding
Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English.
Semantic Graphs for Generating Deep Questions
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.
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
Research into the area of multiparty dialog has grown considerably over recent years.
Ranked #7 on
Discourse Parsing
on Molweni
SciWING -- A Software Toolkit for Scientific Document Processing
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.
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models.
A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages
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.
Sentiment Aware Neural Machine Translation
We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods.
What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?
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.
The CL-SciSumm Shared Task 2018: Results and Key Insights
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.
Overview and Results: CL-SciSumm Shared Task 2019
All papers are from the open access research papers in the CL domain.
Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture
Users participate in online discussion forums to learn from others and share their knowledge with the community.
Dataset Mention Extraction and Classification
Datasets are integral artifacts of empirical scientific research.
Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction
Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures.
When to reply? Context Sensitive Models to Predict Instructor Interventions in MOOC Forums
We propose novel attention based models to infer the amount of latent context necessary to predict instructor intervention.
Recent Advances in Neural Question Generation
Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition.
Resource Mention Extraction for MOOC Discussion Forums
We propose the novel problem of learning resource mention identification in MOOC forums.
Neural Multi-Task Learning for Citation Function and Provenance
Citation function and provenance are two cornerstone tasks in citation analysis.
Treatment Side Effect Prediction from Online User-generated Content
With Health 2. 0, patients and caregivers increasingly seek information regarding possible drug side effects during their medical treatments in online health communities.
Identifying Emergent Research Trends by Key Authors and Phrases
Identifying emergent research trends is a key issue for both primary researchers as well as secondary research managers.
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures
Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility.
Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach
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
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
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
We describe an end-to-end pipeline processing approach for SemEval 2017{'}s Task 10 to extract keyphrases and their relations from scientific publications.
Using Discourse Signals for Robust Instructor Intervention Prediction
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
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.
QANUS: An Open-source Question-Answering Platform
In this paper, we motivate the need for a publicly available, generic software framework for question-answering (QA) systems.
Linear Segmentation and Segment Significance
We present a new method for discovering a segmental discourse structure of a document while categorizing segment function.
