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Found 52 papers, 21 papers with code
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks
This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains.
Knowledge Transfer with Visual Prompt in multi-modal Dialogue Understanding and Generation
In this work, we propose a knowledge transfer method with visual prompt (VPTG) fusing multi-modal data, which is a flexible module that can utilize the text-only seq2seq model to handle visual dialogue tasks.
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing
Text-to-SQL aims to parse natural language questions into SQL queries, which is valuable in providing an easy interface to access large databases.
LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse Dictionary
This paper introduces the approach of Team LingJing’s experiments on SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings (CODWOE).
LingJing at SemEval-2022 Task 3: Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer
For the classification sub-task, we adopt the DeBERTa-v3 pre-trained model for fine-tuning datasets of different languages.
Incremental Intent Detection for Medical Domain with Contrast Replay Networks
We first formulate incremental learning for medical intent detection.
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts
In fact, the semantics of a relation can be expressed by three kinds of graphs: factual graph, ontology graph, textual description graph, and they can complement each other.
BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue Generation
This approach eliminates the need for entity annotation and increases the transparency of the MDG process by explicitly generating the intermediate reasoning chain.
Imagination Augmented Generation: Learning to Imagine Richer Context for Question Answering over Large Language Models
Retrieval-Augmented-Generation and Gener-ation-Augmented-Generation have been proposed to enhance the knowledge required for question answering over Large Language Models (LLMs).
ItD: Large Language Models Can Teach Themselves Induction through Deduction
Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited ability to conduct induction.
From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs
With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support.
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models
Fine-tuning is often necessary to enhance the adaptability of Large Language Models (LLM) to downstream tasks.
ControlLM: Crafting Diverse Personalities for Language Models
This heightens the need to control model behaviors.
ExpNote: Black-box Large Language Models are Better Task Solvers with Experience Notebook
Black-box Large Language Models (LLMs) have shown great power in solving various tasks and are considered general problem solvers.
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders
Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization.
Efficient Data Learning for Open Information Extraction with Pre-trained Language Models
Open Information Extraction (OpenIE) is a fundamental yet challenging task in Natural Language Processing, which involves extracting all triples (subject, predicate, object) from a given sentence.
S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models
The rapid development of Large Language Models (LLMs) has led to great strides in model capabilities like long-context understanding and reasoning.
TableQAKit: A Comprehensive and Practical Toolkit for Table-based Question Answering
Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the questions.
Query2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs
However, these methods train KG embeddings and neural set operators concurrently on both simple (one-hop) and complex (multi-hop and logical) queries, which causes performance degradation on simple queries and low training efficiency.
MMHQA-ICL: Multimodal In-context Learning for Hybrid Question Answering over Text, Tables and Images
Most importantly, we propose a Type-specific In-context Learning Strategy for MMHQA, enabling LLMs to leverage their powerful performance in this task.
LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to increase.
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database
In Textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps.
S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering
In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner.
Large Language Models Need Holistically Thought in Medical Conversational QA
The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care.
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks
Our work highlights the potential of seamlessly unifying explicit rule learning via CoNNs and implicit pattern learning in LMs, paving the way for true symbolic comprehension capabilities.
Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs.
Large Language Models are Better Reasoners with Self-Verification
By performing a backward verification of the answers that LLM deduced for itself, we can obtain interpretable answer validation scores to select the candidate answer with the highest score.
ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains
Recently, natural language database (NLDB) conducts complex QA in knowledge base with textual evidences rather than structured representations, this task attracts a lot of attention because of the flexibility and richness of textual evidence.
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder
In the real-world question answering scenarios, hybrid form combining both tabular and textual contents has attracted more and more attention, among which numerical reasoning problem is one of the most typical and challenging problems.
LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic.
ADBCMM : Acronym Disambiguation by Building Counterfactuals and Multilingual Mixing
To address the above issue, this paper proposes an new method for acronym disambiguation, named as ADBCMM, which can significantly improve the performance of low-resource languages by building counterfactuals and multilingual mixing.
Lifelong Intent Detection via Multi-Strategy Rebalancing
In this paper, we propose Lifelong Intent Detection (LID), which continually trains an ID model on new data to learn newly emerging intents while avoiding catastrophically forgetting old data.
Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion
To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC.
Pre-trained Language Model Based Active Learning for Sentence Matching
Active learning is able to significantly reduce the annotation cost for data-driven techniques.
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning
Existing works didn{'}t consider the extraction order of relational facts in a sentence.
Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss
Meanwhile, such generated question can express the given predicate and correspond to a definitive answer.
Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots
Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC.
Copy-Enhanced Heterogeneous Information Learning for Dialogue State Tracking
How to integrate the semantic information of pre-defined ontology and dialogue text (heterogeneous texts) to generate unknown values and improve performance becomes a severe challenge.
Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation
To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN) which is able to incorporate multi-pass encoding and decoding with multi-level vocabularies into response generation.
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing
To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens.
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases
Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks.
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
The relational facts in sentences are often complicated.
Ranked #12 on
Relation Extraction
on NYT11-HRL
IJCNLP-2017 Task 5: Multi-choice Question Answering in Examinations
The IJCNLP-2017 Multi-choice Question Answering(MCQA) task aims at exploring the performance of current Question Answering(QA) techniques via the realworld complex questions collected from Chinese Senior High School Entrance Examination papers and CK12 website1.
Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning
Generating answer with natural language sentence is very important in real-world question answering systems, which needs to obtain a right answer as well as a coherent natural response.
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge
This simple representation strategy is not easy to express the proper information in the question.
Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?
As one of the most important test of China, Gaokao is designed to be difficult enough to distinguish the excellent high school students.
Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information
With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important.
Knowledge Graph Embedding via Dynamic Mapping Matrix
Ranked #9 on
Link Prediction
on FB15k
(MR metric)
