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.
Conversational Question Answering (ConvQA) is required to answer the current question, conditioned on the observable paragraph-level context and conversation history.
In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials.
自然语言文本中存在大量否定语义表达, 否定焦点识别任务作为更细粒度的否定语义分析, 近年来开始受到自然语言处理学者的关注。该任务旨在识别句子中被否定词修饰和强调的文本片段, 其对自然语言处理的下游任务, 如情感分析、观点挖掘等具有重要意义。与英语相比, 目前面向汉语的否定焦点识别研究彶展缓慢, 其主要原因是尚未有中文数据集为模型提供训练和测试数据。为解决上述问题, 本文在汉语否定与不确定语料库上进行了否定焦点的标注工作, 初步探索了否定焦点在汉语上的语言现象, 并构建了一个包含5, 762个样本的数据集。同时, 本文还提出了一个基于神经网络模型的基线系统, 为后续相关研究提供参照。
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。
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.
Recent techniques in Question Answering (QA) have gained remarkable performance improvement with some QA models even surpassed human performance.
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.
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.
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.
The current aspect extraction methods suffer from boundary errors.
Ranked #2 on Aspect Extraction on SemEval-2016 Task 5 Subtask 1
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.
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.