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Found 8 papers, 5 papers with code
Refining Raw Sentence Representations for Textual Entailment Recognition via Attention
Finally it combines the refined representations of both sentences into a single vector to be used for classification.
Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN
These results, as well as further experiments on domain adaptation for aspect extraction, suggest that differences between speech and written text, which have been discussed extensively in the literature, also extend to the domain of product reviews, where they are relevant for fine-grained opinion mining.
IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets
In this paper we introduce our system for the task of Irony detection in English tweets, a part of SemEval 2018.
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task (IEST), which obtained 2$^{\text{nd}}$ place out of 26 teams with a test macro F1 score of $0. 710$.
Deep contextualized word representations for detecting sarcasm and irony
Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components.
Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study
In this paper we study how different ways of combining character and word-level representations affect the quality of both final word and sentence representations.
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews.
