Search Results for author:
Found 73 papers, 30 papers with code
Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning
Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training.
Emotion Inference in Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy
Emotion inference in multi-turn conversations aims to predict the participant’s emotion in the next upcoming turn without knowing the participant’s response yet, and is a necessary step for applications such as dialogue planning.
WinoLogic: A Zero-Shot Logic-based Diagnostic Dataset for Winograd Schema Challenge
To better evaluate NLMs, we propose a logic-based framework that focuses on high-quality commonsense knowledge.
Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference
We introduce a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision.
Interpretable Low-Resource Legal Decision Making
In this work, we utilize deep learning models in the area of trademark law to shed light on the issue of likelihood of confusion between trademarks.
How Curriculum Learning Impacts Model Calibration
Our studies suggest the following: most of the time curriculum learning has a negligible effect on calibration, but in certain cases under the context of limited training time and noisy data, curriculum learning can substantially reduce calibration error in a manner that cannot be explained by dynamically sampling the dataset.
Exploring Decomposition for Table-based Fact Verification
Fact verification based on structured data is challenging as it requires models to understand both natural language and symbolic operations performed over tables.
Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization
The training method updates parameters of a trained NCMs on two small sets with newly maintained and removed samples, respectively.
Unsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference
There have been two lines of approaches that can be used to further address the limitation: (1) unsupervised pretraining can leverage knowledge in much larger unstructured text data; (2) structured (often human-curated) knowledge has started to be considered in neural-network-based models for NLI.
Unsupervised Conversation Disentanglement through Co-Training
For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier.
Detecting Speaker Personas from Conversational Texts
This is a many-to-many semantic matching task because both contexts and personas in SPD are composed of multiple sentences.
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Enhancing Descriptive Image Captioning with Natural Language Inference
In this paper we propose a novel approach to encourage captioning models to produce more detailed captions using natural language inference, based on the motivation that, among different captions of an image, descriptive captions are more likely to entail less descriptive captions.
THiFly\_Queens at SemEval-2021 Task 9: Two-stage Statement Verification with Adaptive Ensembling and Slot-based Operation
This paper describes our system for verifying statements with tables at SemEval-2021 Task 9.
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.
Ranked #1 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning
Given a passage and the corresponding question, a participating system is expected to choose the correct answer from five candidates of abstract concepts in a cloze-style machine reading comprehension setup.
Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots
Empirical studies on the Persona-Chat dataset show that the partner personas neglected in previous studies can improve the accuracy of response selection in the IMN- and BERT-based models.
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning
In this paper, we explore to improve pretrained models with label hierarchies on the ZS-MTC task.
Multi Label Text Classification
Multi-Label Text Classification
+1
Improving Text-to-SQL with Schema Dependency Learning
In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas.
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
Semantic parsing has long been a fundamental problem in natural language processing.
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems
Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to generate response given dialogue history and knowledge access.
Exploring End-to-End Differentiable Natural Logic Modeling
We explore end-to-end trained differentiable models that integrate natural logic with neural networks, aiming to keep the backbone of natural language reasoning based on the natural logic formalism while introducing subsymbolic vector representations and neural components.
Deriving Commonsense Inference Tasks from Interactive Fictions
Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an indispensable cornerstone in building general AI systems.
Program Enhanced Fact Verification with Verbalization and Graph Attention Network
Built on that, we construct the graph attention verification networks, which are designed to fuse different sources of evidences from verbalized program execution, program structures, and the original statements and tables, to make the final verification decision.
SemEval-2020 Task 5: Counterfactual Recognition
Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not.
SemEval-2020 Task 4: Commonsense Validation and Explanation
In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a natural language statement that makes sense to humans from one that does not, and provide the reasons.
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment
Existing end-to-end dialog systems perform less effectively when data is scarce.
Improving Image Captioning with Better Use of Caption
Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community.
Improving Image Captioning with Better Use of Captions
Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community.
Dynamic Memory Induction Networks for Few-Shot Text Classification
This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot text classification.
Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots
The challenges of building knowledge-grounded retrieval-based chatbots lie in how to ground a conversation on its background knowledge and how to match response candidates with both context and knowledge simultaneously.
DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement
Disentanglement is a problem in which multiple conversations occur in the same channel simultaneously, and the listener should decide which utterance is part of the conversation he will respond to.
Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots.
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
We propose the new problem of learning to recover reasoning chains from weakly supervised signals, i. e., the question-answer pairs.
Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems
The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7.
Ranked #1 on
Conversation Disentanglement
on irc-disentanglement
Learning Cross-modal Context Graph for Visual Grounding
To address their limitations, this paper proposes a language-guided graph representation to capture the global context of grounding entities and their relations, and develop a cross-modal graph matching strategy for the multiple-phrase visual grounding task.
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning
Identifying anomalous samples from highly complex and unstructured data is a crucial but challenging task in a variety of intelligent systems.
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots
Compared with previous persona fusion approaches which enhance the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates.
Exploring Deep Anomaly Detection Methods Based on Capsule Net
In this paper, we develop and explore deep anomaly detection techniques based on the capsule network (CapsNet) for image data.
Deep Learning for Natural Language Inference
This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting-edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning models for language understanding and reasoning.
Several Experiments on Investigating Pretraining and Knowledge-Enhanced Models for Natural Language Inference
Natural language inference (NLI) is among the most challenging tasks in natural language understanding.
Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge
Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning.
Induction Networks for Few-Shot Text Classification
Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries.
Ranked #1 on
Few-Shot Text Classification
on ODIC 5-way (10-shot)
Promoting Diversity for End-to-End Conversation Response Generation
At this stage, two different models are proposed, i. e., a variational generative (VariGen) model and a retrieval based (Retrieval) model.
Logographic Subword Model for Neural Machine Translation
A novel logographic subword model is proposed to reinterpret logograms as abstract subwords for neural machine translation.
Enhancing Sentence Embedding with Generalized Pooling
This paper explores generalized pooling methods to enhance sentence embedding.
Ranked #10 on
Sentiment Analysis
on Yelp Fine-grained classification
Natural Language Inference with External Knowledge
Modeling informal inference in natural language is very challenging.
Neural Natural Language Inference Models Enhanced with External Knowledge
With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance.
Ranked #18 on
Natural Language Inference
on SNLI
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task.
Ranked #68 on
Natural Language Inference
on SNLI
Deep Learning for Semantic Composition
Learning representation to model the meaning of text has been a core problem in NLP.
A Dataset for Multi-Target Stance Detection
Current models for stance classification often treat each target independently, but in many applications, there exist natural dependencies among targets, e. g., stance towards two or more politicians in an election or towards several brands of the same product.
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA).
Ranked #40 on
Question Answering
on SQuAD1.1 dev
Extracting Discriminative Keyphrases with Learned Semantic Hierarchies
We show that such knowledge can be used to construct better discriminative keyphrase extraction systems that do not assume a static, fixed set of keyphrases for a document.
Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge
The PDP task we investigate in this paper is a complex coreference resolution task which requires the utilization of commonsense knowledge.
Distraction-Based Neural Networks for Document Summarization
Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences.
Enhanced LSTM for Natural Language Inference
Reasoning and inference are central to human and artificial intelligence.
Ranked #27 on
Natural Language Inference
on SNLI
A Dataset for Detecting Stance in Tweets
Apart from stance, the tweets are also annotated for whether the target of interest is the target of opinion in the tweet.
Probabilistic Reasoning via Deep Learning: Neural Association Models
We propose to use neural networks to model association between any two events in a domain.
A Deep Learning Model for Structured Outputs with High-order Interaction
Many real-world applications are associated with structured data, where not only input but also output has interplay.
Long Short-Term Memory Over Tree Structures
The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation.
Measuring academic influence: Not all citations are equal
Unlike the conventional h-index, it weights citations by how many times a reference is mentioned.
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of a term within a submissions stood first in both tasks on tweets, obtaining an F-score of 69. 02 in the message-level task and 88. 93 in the term-level task.
Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010
Objective: As clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality.
Ranked #5 on
Clinical Concept Extraction
on 2010 i2b2/VA
