全部 NLP 人脸识别 医疗图像 图像识别 智能交通 机器学习 机器视觉 自动驾驶 语音识别
分类：
排序
最新发布 最新更新 查看最多 评论最多
QASC: A Dataset for Question Answering via Sentence CompositionComposing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.

QASC: A Dataset for Question Answering via Sentence CompositionComposing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.

折后：￥0元

原价
￥0元 VIP价
￥0 节省
￥0元
暂无演示 下载资源
Reasoning Over Paragraph Effects in Situations (ROPES)

Reasoning Over Paragraph Effects in Situations (ROPES)

折后：￥0元

原价
￥0元 VIP价
￥0 节省
￥0元
暂无演示 下载资源
Flickr Personal Taxonomies

Flickr Personal Taxonomies

折后：￥0元

原价
￥0元 VIP价
￥0 节省
￥0元
暂无演示 下载资源
SouthparkData

SouthparkData

折后：￥0元

原价
￥0元 VIP价
￥0 节省
￥0元
暂无演示 下载资源
路透社语料库

路透社语料库

折后：￥0元

原价
￥0元 VIP价
￥0 节省
￥0元
暂无演示 下载资源
课程科珀斯采矿和多阶段微调，以改善讲座翻译

课程科珀斯采矿和多阶段微调，以改善讲座翻译

折后：￥0元

原价
￥0元 VIP价
￥0 节省
￥0元
暂无演示 下载资源
德克斯特数据集

德克斯特数据集

折后：￥0元

原价
￥0元 VIP价
￥0 节省
￥0元
暂无演示 下载资源
DNA Methylation Corpus

DNA Methylation Corpus

折后：￥0元

原价
￥0元 VIP价
￥0 节省
￥0元
暂无演示 下载资源
Apache Software Foundation公共邮件存档

Apache Software Foundation公共邮件存档

折后：￥0元

原价
￥0元 VIP价
￥0 节省
￥0元
暂无演示 下载资源
识别文本中的关键短语

识别文本中的关键短语

折后：￥0元

原价
￥0元 VIP价
￥0 节省
￥0元
暂无演示 下载资源
123456...23