Papers by Paulo Cavalin
Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes (2020.emnlp-main)
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| Challenge: | Existing methods for intent classification use one-class classification or inverse dictionary. |
| Approach: | They propose to represent class labels as a vector space where word graphs are mapped . they use inverse dictionary to take in account inter-class similarities provided by repeated occurrences . |
| Outcome: | The proposed method beats the state-of-the-art method in the Larson dataset by about 31 percentage points. |
Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems (2021.acl-long)
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Claudio Pinhanez, Paulo Cavalin, Victor Henrique Alves Ribeiro, Ana Appel, Heloisa Candello, Julio Nogima, Mauro Pichiliani, Melina Guerra, Maira de Bayser, Gabriel Malfatti, Henrique Ferreira
| Challenge: | a recent study shows that meta-knowledge embedded in intent identifiers improves intent recognition in conversational systems . a meta-learning approach is used to classify sentences into discrete sets of classes . classification is a key part of professional conversational system implementations . |
| Approach: | They use meta-knowledge embedded in intent identifiers to improve intent recognition . authors found that meta-knowledge improved accuracy in conversational systems . |
| Outcome: | The meta-knowledge enabled improved intent recognition in conversational systems . the meta-learning improved the false acceptance rate in two thirds of the chatbots . |
Theoretical and Empirical Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Open-World Scenarios (2024.lrec-main)
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| Challenge: | Existing methods for encoding intents as one-hot are not used for zero-shot learning. |
| Approach: | They show that one-hot encodings can produce much richer topologies for OOS detection . they argue that such gains are likely due to advantages of knowledge-free encoded intents . |
| Outcome: | The proposed method can produce better systems for open-world classification tasks, the authors show . they show that knowledge-free, randomly generated dense-vector encodings can produce 20% gains over one-hot encodes. |
Fixing Rogue Memorization in Many-to-One Multilingual Translators of Extremely-Low-Resource Languages by Rephrasing Training Samples (2024.naacl-long)
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| Challenge: | Existing fine-tuning of large high-resource language models into multilingual machine translators is difficult for extremely lowresource languages. |
| Approach: | They propose to fine-tune large high-resource language models into multilingual machine translators for extremely-lowresource languages such as endangered Indigenous languages. |
| Outcome: | The proposed model halls are reformulated to improve translation accuracy and improve translation quality. |
From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer (2020.findings-emnlp)
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| Challenge: | Existing methods for sentiment transfer have relied on unsupervised methods due to lack of parallel corpora. |
| Approach: | They propose a method for creating parallel data to train Seq2Seq neural networks for sentiment transfer. |
| Outcome: | The proposed method outperforms existing unsupervised methods in sentiment transfer tasks. |