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Found 23 papers, 8 papers with code
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation
In addition, we propose Conv-Distinct, a novel evaluation metric for CQG, to evaluate the diversity of the generated conversation from a context.
Capturing Conversational Interaction for Question Answering via Global History Reasoning
Conversational Question Answering (ConvQA) is required to answer the current question, conditioned on the observable paragraph-level context and conversation history.
Adversarial Feature Adaptation for Cross-lingual Relation Classification
In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data.
CoHS-CQG: Context and History Selection for Conversational Question Generation
While previous studies mainly focus on how to model the flow and alignment of the conversation, there has been no thorough study to date on which parts of the context and history are necessary for the model.
KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question Answering
Within the experimental models, the T5-based GenCQA with KEPR obtains the best performance, which is up to 60. 91% at the primary canonical metric Inc@3.
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension
Most existing RC models are developed on formal datasets such as news articles and Wikipedia documents, which severely limit their performances when directly applied to the noisy and informal texts in social media.
Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition
In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events.
Negative Focus Detection via Contextual Attention Mechanism
For the sake of understanding a negated statement, it is critical to precisely detect the negative focus in context.
Don't Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction
The current aspect extraction methods suffer from boundary errors.
Ranked #2 on
Aspect Extraction
on SemEval 2015 Task 12
GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking
As an essential component of task-oriented dialogue systems, Dialogue State Tracking (DST) takes charge of estimating user intentions and requests in dialogue contexts and extracting substantial goals (states) from user utterances to help the downstream modules to determine the next actions of dialogue systems.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
Multi-grained Chinese Word Segmentation with Weakly Labeled Data
Detailed evaluation shows that our proposed model with weakly labeled data significantly outperforms the state-of-the-art MWS model by 1. 12 and 5. 97 on NEWS and BAIKE data in F1.
Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation
Complex question answering over knowledge base remains as a challenging task because it involves reasoning over multiple pieces of information, including intermediate entities/relations and other constraints.
Winnowing Knowledge for Multi-choice Question Answering
However, the current reasoning models suffer from the noises in the retrieved knowledge.
基于多任务学习的生成式阅读理解(Generative Reading Comprehension via Multi-task Learning)
生成式阅读理解是机器阅读理解领域一项新颖且极具挑战性的研究。与主流的抽取式阅读理解相比, 生成式阅读理解模型不再局限于从段落中抽取答案, 而是能结合问题和段落生成自然和完整的表述作为答案。然而, 现有的生成式阅读理解模型缺乏对答案在段落中的边界信息以及对问题类型信息的理解。为解决上述问题, 本文提出一种基于多任务学习的生成式阅读理解模型。该模型在训练阶段将答案生成任务作为主任务, 答案抽取和问题分类任务作为辅助任务进行多任务学习, 同时学习和优化模型编码层参数;在测试阶段加载模型编码层进行解码生成答案。实验结果表明, 答案抽取模型和问题分类模型能够有效提升生成式阅读理解模型的性能。
Improving Lexical Embeddings for Robust Question Answering
Recent techniques in Question Answering (QA) have gained remarkable performance improvement with some QA models even surpassed human performance.
Automatic True/False Question Generation for Educational Purpose
In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials.
Coreference-aware Double-channel Attention Network for Multi-party Dialogue Reading Comprehension
The corresponding solutions contribute to the answer-oriented reasoning on a series of well-organized and thread-aware conversational contexts.
UFO: Unified Fact Obtaining for Commonsense Question Answering
UFO turns LLMs into knowledge sources and produces relevant facts (knowledge statements) for the given question.
Evidence-based Interpretable Open-domain Fact-checking with Large Language Models
Universal fact-checking systems for real-world claims face significant challenges in gathering valid and sufficient real-time evidence and making reasoned decisions.
