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Found 9 papers, 4 papers with code
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)
“目前常见的机器阅读理解数据增强方法如回译, 单独对文章或者问题进行数据增强, 没有考虑文章、问题和选项三元组之间的联系。因此, 本文探索了一种利用三元组联系进行文章句子筛选的数据增强方法, 通过比较文章与问题以及选项的相似度, 选取文章中与二者联系紧密的句子。同时为了使不同选项的三元组区别增大, 我们选用了正则化Dropout的策略。实验结果表明, 在RACE数据集上的准确率可提高3. 8%。”
Towards Imperceptible Document Manipulations against Neural Ranking Models
Additionally, current methods rely heavily on the use of a well-imitated surrogate NRM to guarantee the attack effect, which makes them difficult to use in practice.
Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective
Knowledge graph completion (KGC) aims to infer missing knowledge triples based on known facts in a knowledge graph.
Towards Quantifiable Dialogue Coherence Evaluation
To address these limitations, we propose Quantifiable Dialogue Coherence Evaluation (QuantiDCE), a novel framework aiming to train a quantifiable dialogue coherence metric that can reflect the actual human rating standards.
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context
BERT-based text ranking models have dramatically advanced the state-of-the-art in ad-hoc retrieval, wherein most models tend to consider individual query-document pairs independently.
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems
Capitalized on the topic-level dialogue graph, we propose a new evaluation metric GRADE, which stands for Graph-enhanced Representations for Automatic Dialogue Evaluation.
Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation
Target-guided open-domain conversation aims to proactively and naturally guide a dialogue agent or human to achieve specific goals, topics or keywords during open-ended conversations.
Natural Language Comprehension with the EpiReader
We present the EpiReader, a novel model for machine comprehension of text.
Ranked #7 on
Question Answering
on Children's Book Test
A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data
The parallel hierarchy enables our model to compare the passage, question, and answer from a variety of trainable perspectives, as opposed to using a manually designed, rigid feature set.
Ranked #1 on
Question Answering
on MCTest-160
