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Found 15 papers, 5 papers with code
Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension
To facilitate the model's capabilities to better differentiate the reasoning process associated with each option, we introduce a novel thought-path contrastive learning method that compares reasoning paths between the original and counterfactual samples.
Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition
Essentially, our method seamlessly injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction.
Teach model to answer questions after comprehending the document
Multi-choice Machine Reading Comprehension (MRC) is a challenging extension of Natural Language Processing (NLP) that requires the ability to comprehend the semantics and logical relationships between entities in a given text.
Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition
Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse.
Intra-Correlation Encoding for Chinese Sentence Intention Matching
Sentence intention matching is vital for natural language understanding.
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection
As conventional answer selection (AS) methods generally match the question with each candidate answer independently, they suffer from the lack of matching information between the question and the candidate.
Tag Recommendation by Word-Level Tag Sequence Modeling
In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model.
Neural Chinese Word Segmentation as Sequence to Sequence Translation
In this paper, we cast the CWS as a sequence translation problem and propose a novel sequence-to-sequence CWS model with an attention-based encoder-decoder framework.
Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning
To alleviate this problem, we propose an NMT approach that heightens the adequacy in machine translation by transferring the semantic knowledge learned from bilingual sentence alignment.
Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations.
Induction Networks for Few-Shot Text Classification
Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries.
Ranked #1 on
Few-Shot Text Classification
on ODIC 5-way (10-shot)
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base
The techniques of our runs mainly make use of the word embeddings and the knowledge-based method.
BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity
This paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task.
