Papers with sentence encoding

3 papers
Exploiting WordNet Synset and Hypernym Representations for Answer Selection (2020.aacl-main)

Copied to clipboard

Challenge: Answer selection (AS) is a challenging subtask of document-based question answering (DQA).
Approach: They propose to use WordNet to enrich the word representation and sentence encoding to incorporate similarity scores of two concepts that share synset or hypernym relations into the attention mechanism.
Outcome: The proposed model outperforms existing state-of-the-art models on the public WikiQA and SelQA datasets and significantly improves the baseline system.
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis (D19-1)

Copied to clipboard

Challenge: Existing models use aspect-independent encoders for sentence representation generation.
Approach: They propose an aspect-guided deep transition model which guides the sentence encoding from scratch with a specially-designed deep transition architecture.
Outcome: The proposed model outperforms existing models on multiple datasets on aspect-category sentiment analysis and aspectterm sentiment analysis without additional features.
Comparative Study of Sentence Embeddings for Contextual Paraphrasing (2020.lrec-1)

Copied to clipboard

Challenge: Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented.
Approach: They propose to use contextual paraphrasing to capture the meaning of a sentence while performing dialogue act clustering.
Outcome: The proposed task combines paraphrases with dialogue act clustering to capture such contextual paraphrasing.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations