Taha Aksu - Academia.edu (original) (raw)
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Homi Bhabha National Institute (HBNI, BARC, MUMBAI)
UERJ - Universidade do Estado do Rio de Janeiro / Rio de Janeiro State University
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Papers by Taha Aksu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without ... more A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data-zero-shot domain adaptation. Parameter-Efficient Transfer Learning (PETL) has the potential to address this problem due to its robustness. However, it has yet to be applied to the zero-shot scenarios, as it is not clear how to apply it unsupervisedly. Our method, Prompter, uses descriptions of target domain slots to generate dynamic prefixes that are concatenated to the key and values at each layer's self-attention mechanism. This allows for the use of prefix-tuning in zeroshot. Prompter outperforms previous methods on both the MultiWOZ and SGD benchmarks. In generating prefixes, our analyses find that Prompter not only utilizes the semantics of slot descriptions but also how often the slots appear together in conversation. Moreover, Prompter's gains are due to its improved ability to distinguish "none"-valued dialogue slots, compared against baselines.
Lecture Notes in Electrical Engineering
As the creation of task-oriented conversational data is costly, data augmentation techniques have... more As the creation of task-oriented conversational data is costly, data augmentation techniques have been proposed to create synthetic data to improve model performance in new domains. Up to now, these learning-based techniques (e.g. paraphrasing) still require a moderate amount of data, making application to low-resource settings infeasible. To tackle this problem, we introduce an augmentation framework that creates synthetic task-oriented dialogues, operating with as few as 5 shots. Our framework utilizes belief state annotations to define dialogue functions of each turn pair. It then creates templates of pairs through de-lexicalization, where the dialogue function codifies the allowable incoming and outgoing links of each template. To generate new dialogues, our framework composes allowable adjacent templates in a bottom-up manner. We evaluate our framework using TRADE as the base DST model, observing significant improvements in the fine-tuning scenarios within a low-resource settin...
We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus ... more We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus availability problem for dialogue comprehension. Velocidapter augments datasets by simulating synthetic conversations for a task-oriented dialogue domain, requiring a small amount of bootstrapping work for each new domain. We evaluate the efficacy of our framework on a task-oriented dialogue comprehension dataset, MRCWOZ, which we curate by annotating questions for slots in the restaurant, taxi, and hotel domains of the MultiWOZ 2.2 dataset (Zang et al., 2020). We run experiments within a low-resource setting, where we pretrain a model on SQuAD, fine-tuning it on either a small original data or on the synthetic data generated by our framework. Velocidapter shows significant improvements using both the transformer-based BERTBase and BiDAF as base models. We further show that the framework is easy to use by novice users and conclude that Velocidapter can greatly help training over task-ori...
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without ... more A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data-zero-shot domain adaptation. Parameter-Efficient Transfer Learning (PETL) has the potential to address this problem due to its robustness. However, it has yet to be applied to the zero-shot scenarios, as it is not clear how to apply it unsupervisedly. Our method, Prompter, uses descriptions of target domain slots to generate dynamic prefixes that are concatenated to the key and values at each layer's self-attention mechanism. This allows for the use of prefix-tuning in zeroshot. Prompter outperforms previous methods on both the MultiWOZ and SGD benchmarks. In generating prefixes, our analyses find that Prompter not only utilizes the semantics of slot descriptions but also how often the slots appear together in conversation. Moreover, Prompter's gains are due to its improved ability to distinguish "none"-valued dialogue slots, compared against baselines.
Lecture Notes in Electrical Engineering
As the creation of task-oriented conversational data is costly, data augmentation techniques have... more As the creation of task-oriented conversational data is costly, data augmentation techniques have been proposed to create synthetic data to improve model performance in new domains. Up to now, these learning-based techniques (e.g. paraphrasing) still require a moderate amount of data, making application to low-resource settings infeasible. To tackle this problem, we introduce an augmentation framework that creates synthetic task-oriented dialogues, operating with as few as 5 shots. Our framework utilizes belief state annotations to define dialogue functions of each turn pair. It then creates templates of pairs through de-lexicalization, where the dialogue function codifies the allowable incoming and outgoing links of each template. To generate new dialogues, our framework composes allowable adjacent templates in a bottom-up manner. We evaluate our framework using TRADE as the base DST model, observing significant improvements in the fine-tuning scenarios within a low-resource settin...
We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus ... more We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus availability problem for dialogue comprehension. Velocidapter augments datasets by simulating synthetic conversations for a task-oriented dialogue domain, requiring a small amount of bootstrapping work for each new domain. We evaluate the efficacy of our framework on a task-oriented dialogue comprehension dataset, MRCWOZ, which we curate by annotating questions for slots in the restaurant, taxi, and hotel domains of the MultiWOZ 2.2 dataset (Zang et al., 2020). We run experiments within a low-resource setting, where we pretrain a model on SQuAD, fine-tuning it on either a small original data or on the synthetic data generated by our framework. Velocidapter shows significant improvements using both the transformer-based BERTBase and BiDAF as base models. We further show that the framework is easy to use by novice users and conclude that Velocidapter can greatly help training over task-ori...