Papers with representation learning process
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders (2020.emnlp-main)
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| Challenge: | Named entity recognition and relation extraction are two important fundamental problems. |
| Approach: | They propose to design two separate encoders to capture two different types of information in the representation learning process. |
| Outcome: | The proposed methods show significant improvements on standard datasets. |
Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction (2024.emnlp-main)
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| Challenge: | Dialog2Flow embeddings allow for modeling dialogs as continuous trajectories in a latent space with distinct action-related regions. |
| Approach: | They propose dialog2Flow embeddings that map dialogs to a latent space and cluster them according to their communicative and informative functions. |
| Outcome: | The proposed workflow embeddings show superior performance across domains. |
Robust Representation Learning of Biomedical Names (P19-1)
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| Challenge: | Biomedical concepts are often mentioned in medical documents under different name variations. |
| Approach: | They propose a framework for learning robust representations of biomedical names and terms . they encode contextual meaning, conceptual meaning, and similarity between synonyms . |
| Outcome: | The proposed framework outperforms baselines on retrieval, similarity and relatedness benchmarks. |
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification (D18-1)
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| Challenge: | Existing sentiment lexicons do not handle word sense and the concept of semantic compositionality is non-existent in simple lexiconic approaches. |
| Approach: | They propose a lexicon-driven contextual attention mechanism and a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. |
| Outcome: | The proposed model outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets. |
Dynamic Open-book Prompt for Conversational Recommender System (2023.findings-emnlp)
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| Challenge: | Existing methods for prompt learning use only training samples for parameter training, limiting the performance of existing methods. |
| Approach: | They propose a Dynamic Open-book Prompt approach where the open book stores user's experiences in historical data and dynamically constructs the prompt to memorize the user' s current utterance. |
| Outcome: | The proposed model improves on the existing methods on the ReDial dataset and shows that it can be used to learn contextually relevant recommendations. |
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective (2023.findings-emnlp)
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| Challenge: | Recent studies have identified the severe performance decrease on analogous classes as a key factor for catastrophic forgetting. |
| Approach: | They propose a replay-based continual text classification method that uses fast-slow and current-past contrastive learning to perform mutual information maximization and better recover previously learned representations. |
| Outcome: | The proposed method achieves state-of-the-art on three text classification tasks. |