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Found 21 papers, 6 papers with code
Enhancing Aspect Term Extraction with Soft Prototypes
Aspect term extraction (ATE) aims to extract aspect terms from a review sentence that users have expressed opinions on.
Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing
It is a challenging task since the recognition of the emotion in one utterance involves many complex factors, such as the conversational context, the speaker's background, and the subtle difference between emotion labels.
Prompting Large Language Models for Counterfactual Generation: An Empirical Study
Based on this framework, we 1) investigate the strengths and weaknesses of LLMs as the counterfactual generator, and 2) disclose the factors that affect LLMs when generating counterfactuals, including both the intrinsic properties of LLMs and prompt designing.
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction
However, current generative models lack the optimization process of model generalization on different tasks during training, and thus have limited generalization capability.
Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the overdetected false spans at the span detection stage and the inaccurate and unstable prototypes at the type classification stage remain to be challenging problems.
Ranked #2 on
Few-shot NER
on Few-NERD (INTRA)
Automatically Generating Counterfactuals for Relation Classification
In this paper, we formulate the problem of automatically generating CAD for RC tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities.
From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction
Specifically, we first let the teachers correspond to the multiple models and select the samples in the intersection set of the last iteration in SSRE methods to augment labeled data as usual.
Bridge-Based Active Domain Adaptation for Aspect Term Extraction
Existing methods solve this problem by associating aspect terms with pivot words (we call this passive domain adaptation because the transfer of aspect terms relies on the links to pivots).
Multi-Scale Feature and Metric Learning for Relation Extraction
Specifically, we first develop a multi-scale convolutional neural network to aggregate the non-successive mainstays in the lexical sequence.
Intent Disentanglement and Feature Self-supervision for Novel Recommendation
Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems.
Recommending Accurate and Diverse Items Using Bilateral Branch Network
Specifically, we encode domain level diversity by adaptively balancing accurate recommendation in the conventional branch and diversified recommendation in the adaptive branch of a bilateral branch network.
Exploit Multiple Reference Graphs for Semi-supervised Relation Extraction
To tackle this limitation, we propose to build the connection between the unlabeled data and the labeled ones rather than directly mapping the unlabeled samples to the classes.
Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) involves three subtasks, i. e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+5
Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks
More importantly, for a cold start user/item that does not have any interactions, such methods are unable to learn the preference embedding of the user/item since there is no link to this user/item in the graph.
Seq2seq Translation Model for Sequential Recommendation
We then generalize recent advancements in translation model from sequences of words in two languages to sequences of items and contexts in recommender systems.
Transfer Capsule Network for Aspect Level Sentiment Classification
In this paper, we propose a Transfer Capsule Network (TransCap) model for transferring document-level knowledge to aspect-level sentiment classification.
Enhanced Aspect Level Sentiment Classification with Auxiliary Memory
In our model, a main memory is used to capture the important context words for sentiment classification.
An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection
With the abundant attributes in existing entities and knowledge in other domains, we successfully solve the problem of data scarcity in the cold-start settings.
