Papers with Intent Classification
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks (2022.emnlp-industry)
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| Challenge: | Domain Classification (DC) and Intent Classification/Named Entity Recognition (ICNER) are the most common methods for reducing teacher-student knowledge into manageable sizes for low-latency downstream applications. |
| Approach: | They investigate whether distillation from a generic LM benefits downstream tasks . a domain classification and a task-specific data set are used to fine tune the model . |
| Outcome: | The proposed model improves across tasks and test sets when only task-specific data is used. |
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging (2022.coling-1)
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| Challenge: | LINGUIST generates annotated data for Intent Classification and Slot Tagging (IC+ST) we demonstrate fine-tuning of a large-scale seq2seq model to control outputs of multilingual data generation. |
| Approach: | They propose a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST) they use a 5-billion-parameter multilingual sequence-to-sequence model to fine-tune it on a flexible instruction prompt. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on a SNIPS intent setting and shows significant improvement on IC+ST in a cross-lingual setting. |
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings (2022.coling-1)
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Jason Ingyu Choi, Saar Kuzi, Nikhita Vedula, Jie Zhao, Giuseppe Castellucci, Marcus Collins, Shervin Malmasi, Oleg Rokhlenko, Eugene Agichtein
| Challenge: | Existing Conversational Task Assistants fail to provide a comprehensive natural conversation that includes search, context-aware QA, step-by-step instructions. |
| Approach: | They present a corpus of conversations in two domains: cooking and home improvement . they crowd-sourced 549 conversations with an asynchronous Wizard-of-Oz setup . |
| Outcome: | The proposed model performs well in both Intent Classification and Abstractive Question Answering tasks, but the performance is poor on AQA tasks. |