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        <title>Learning Better Intent Representations for Financial Open Intent Classification</title>
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        <namePart type="given">Xianzhi</namePart>
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        <namePart type="given">Xiaodan</namePart>
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            <title>Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)</title>
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            <namePart type="given">Chung-Chi</namePart>
            <namePart type="family">Chen</namePart>
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            <namePart type="given">Hen-Hsen</namePart>
            <namePart type="family">Huang</namePart>
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            <namePart type="given">Hiroya</namePart>
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            <namePart type="given">Hsin-Hsi</namePart>
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    <abstract>With the recent surge of NLP technologies in the financial domain, banks and other financial entities have adopted virtual agents (VA) to assist customers. A challenging problem for VAs in this domain is determining a user’s reason or intent for contacting the VA, especially when the intent was unseen or open during the VA’s training. One method for handling open intents is adaptive decision boundary (ADB) post-processing, which learns tight decision boundaries from intent representations to separate known and open intents. We propose incorporating two methods for supervised pre-training of intent representations: prefix tuning and fine-tuning just the last layer of a large language model (LLM). With this proposal, our accuracy is 1.63% - 2.07% higher than the prior state-of-the-art ADB method for open intent classification on the banking77 benchmark amongst others. Notably, we only supplement the original ADB model with 0.1% additional trainable parameters. Ablation studies also determine that our method yields better results than full fine-tuning the entire model. We hypothesize that our findings could stimulate a new optimal method of downstream tuning that combines parameter efficient tuning modules with fine-tuning a subset of the base model’s layers.</abstract>
    <identifier type="citekey">li-etal-2022-learning-better</identifier>
    <identifier type="doi">10.18653/v1/2022.finnlp-1.8</identifier>
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            <start>68</start>
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%0 Conference Proceedings
%T Learning Better Intent Representations for Financial Open Intent Classification
%A Li, Xianzhi
%A Aitken, Will
%A Zhu, Xiaodan
%A Thomas, Stephen W.
%Y Chen, Chung-Chi
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F li-etal-2022-learning-better
%X With the recent surge of NLP technologies in the financial domain, banks and other financial entities have adopted virtual agents (VA) to assist customers. A challenging problem for VAs in this domain is determining a user’s reason or intent for contacting the VA, especially when the intent was unseen or open during the VA’s training. One method for handling open intents is adaptive decision boundary (ADB) post-processing, which learns tight decision boundaries from intent representations to separate known and open intents. We propose incorporating two methods for supervised pre-training of intent representations: prefix tuning and fine-tuning just the last layer of a large language model (LLM). With this proposal, our accuracy is 1.63% - 2.07% higher than the prior state-of-the-art ADB method for open intent classification on the banking77 benchmark amongst others. Notably, we only supplement the original ADB model with 0.1% additional trainable parameters. Ablation studies also determine that our method yields better results than full fine-tuning the entire model. We hypothesize that our findings could stimulate a new optimal method of downstream tuning that combines parameter efficient tuning modules with fine-tuning a subset of the base model’s layers.
%R 10.18653/v1/2022.finnlp-1.8
%U https://aclanthology.org/2022.finnlp-1.8
%U https://doi.org/10.18653/v1/2022.finnlp-1.8
%P 68-77

Markdown (Informal)

[Learning Better Intent Representations for Financial Open Intent Classification](https://aclanthology.org/2022.finnlp-1.8) (Li et al., FinNLP 2022)

• Learning Better Intent Representations for Financial Open Intent Classification (Li et al., FinNLP 2022)

ACL

• Xianzhi Li, Will Aitken, Xiaodan Zhu, and Stephen W. Thomas. 2022. Learning Better Intent Representations for Financial Open Intent Classification. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 68–77, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.