Search Results for author:
Found 25 papers, 14 papers with code
Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models
The key challenge of question answering over knowledge bases (KBQA) is the inconsistency between the natural language questions and the reasoning paths in the knowledge base (KB).
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation
CQG employs a simple method to generate the multi-hop questions that contain key entities in multi-hop reasoning chains, which ensure the complexity and quality of the questions.
Table Fact Verification with Structure-Aware Transformer
Verifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning.
Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation
In this paper, we propose Credibility-aware Generation (CAG), a universally applicable framework designed to mitigate the impact of flawed information in RAG.
Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model
The sequential recommendation task aims to predict the item that user is interested in according to his/her historical action sequence.
InstructERC: Reforming Emotion Recognition in Conversation with a Retrieval Multi-task LLMs Framework
The field of emotion recognition of conversation (ERC) has been focusing on separating sentence feature encoding and context modeling, lacking exploration in generative paradigms based on unified designs.
Ranked #2 on
Emotion Recognition in Conversation
on MELD
Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and relations in KBs.
Ranked #2 on
Knowledge Base Question Answering
on WebQuestionsSP
Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering
Equipped with Chain-of-Thought (CoT), Large language models (LLMs) have shown impressive reasoning ability in various downstream tasks.
T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing
Translating natural language queries into SQLs in a seq2seq manner has attracted much attention recently.
Ranked #8 on
Text-To-SQL
on spider
Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering
For each given question, TMA first extracts the relevant concepts from the KG, and then feeds them into a multiway adaptive module to produce a \emph{temporal-specific} representation of the question.
Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts
To alleviate this problem, we propose a novel Dual Path Modeling Framework to enhance the model's ability to perceive subtle differences in sentence pairs by separately modeling affinity and difference semantics.
Robust Lottery Tickets for Pre-trained Language Models
Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching accuracy comparable to the original models.
PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models
A wide range of NLP tasks benefit from the fine-tuning of pretrained language models (PLMs).
Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion
Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching.
DABERT: Dual Attention Enhanced BERT for Semantic Matching
To alleviate this problem, we propose a novel Dual Attention Enhanced BERT (DABERT) to enhance the ability of BERT to capture fine-grained differences in sentence pairs.
Let Me Check the Examples: Enhancing Demonstration Learning via Explicit Imitation
Inspired by the human learning process, in this paper, we introduce Imitation DEMOnstration Learning (Imitation-Demo) to strengthen demonstration learning via explicitly imitating human review behaviour, which includes: (1) contrastive learning mechanism to concentrate on the similar demonstrations.
Searching for Optimal Subword Tokenization in Cross-domain NER
Input distribution shift is one of the vital problems in unsupervised domain adaptation (UDA).
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER
Recently, prompt-based methods have achieved significant performance in few-shot learning scenarios by bridging the gap between language model pre-training and fine-tuning for downstream tasks.
Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL
Conversational text-to-SQL aims at converting multi-turn natural language queries into their corresponding SQL (Structured Query Language) representations.
Ranked #2 on
Text-To-SQL
on SParC
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models
To solve this issue, various data augmentation techniques are proposed to improve the robustness of PLMs.
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer
Learning high-quality sentence representations benefits a wide range of natural language processing tasks.
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension
Weakly supervised machine reading comprehension (MRC) task is practical and promising for its easily available and massive training data, but inevitablely introduces noise.
Leveraging Historical Interaction Data for Improving Conversational Recommender System
Most of the existing CRS methods focus on learning effective preference representations for users from conversation data alone.
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture.
BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation
In this work, we reexamine the inter-related problems of "topic identification" and "text segmentation" for sparse document learning, when there is a single new text of interest.
