Papers by Maxine Eskenazi
DialCrowd 2.0: A Quality-Focused Dialog System Crowdsourcing Toolkit (2022.lrec-1)
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| Challenge: | DialCrowd 2.0 helps requesters obtain higher quality data from human intelligence tasks. |
| Approach: | They propose to use DialCrowd 2.0 to help requesters obtain higher quality data . they aim to improve the way requesters present tasks and facilitate effective communication with workers. |
| Outcome: | The proposed toolkit enables requesters to obtain higher quality data by presenting tasks more clearly and facilitating effective communication with workers. |
“None of the Above”: Measure Uncertainty in Dialog Response Retrieval (2020.acl-main)
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| Challenge: | End-to-end (E2E) dialog retrieval models jointly encode a dialog and a candidate response, assuming the ground truth is always present in the candidate set. |
| Approach: | They propose to capture the original retrieval model's confidence concerning the best prediction using trivial additional computation. |
| Outcome: | The proposed model can capture the model's confidence concerning the best prediction using trivial additional computation. |
USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation (2020.acl-main)
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| Challenge: | Standard language generation metrics have been shown to be ineffective for dialog evaluation. |
| Approach: | They propose an unsupervised evaluation metric for dialog that trains unsupervised models to measure several desirable qualities of dialog. |
| Outcome: | The proposed evaluation metric strongly correlates with human judgment on Topical-Chat and PersonaChat. |
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models (N19-1)
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| Challenge: | Existing approaches to define action spaces for conversational agents have limitations . end-to-end dialog systems can handle complex domains with limited action space . |
| Approach: | They propose a latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods to induce its own action space from the data. |
| Outcome: | The proposed framework achieves better performance than word-level policy gradient methods on DealOrNoDeal and MultiWoz dialogs. |
Multi-Granularity Representations of Dialog (D19-1)
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| Challenge: | Neural models of dialog rely on generalized latent representations of language. |
| Approach: | They propose a training procedure which explicitly learns multiple representations of language at several levels of granularity. |
| Outcome: | The proposed training procedure significantly improves performance on the next utterance retrieval task using the MultiWOZ dataset and the Ubuntu dialog corpus. |
Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation (P18-1)
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| Challenge: | Existing encoder-decoder dialog models cannot output interpretable actions as in traditional systems. |
| Approach: | They propose an unsupervised discrete sentence representation learning method that integrates with existing encoder-decoder dialog models for interpretable response generation. |
| Outcome: | The proposed model can be integrated with existing encoder-decoder dialog models and discover interpretable semantics via either auto encoding or context predicting. |
Pretraining Methods for Dialog Context Representation Learning (P19-1)
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| Challenge: | Existing methods for pretraining dialog context encoders are still in their infancy. |
| Approach: | They propose to use unsupervised pretraining objectives for dialog context representations to fine-tune and evaluate them on a set of downstream dialog tasks. |
| Outcome: | The proposed methods improve performance on a set of dialog tasks and are less data hungry. |
Interactive Evaluation of Dialog Track at DSTC9 (2022.lrec-1)
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| Challenge: | Currently, dialog research is focused on static data, which neglects multiple important properties of dialog, such as consistency, topic depth, adaptation, error recovery and user-centric development. |
| Approach: | They propose to use static dialogs to build strong response generation models and extend them to back-and-forth interactions with real users. |
| Outcome: | The proposed model trains a larger evolved Transformer model on social media data and attains strong performance in interactive settings. |
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning (2022.emnlp-main)
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| Challenge: | Instruction tuning is emerging in NLP, but has not been explored for dialogue-related tasks. |
| Approach: | They propose an instruction tuning framework for dialogue that leverages natural language instructions with language models to induce zero-shot generalization on unseen tasks. |
| Outcome: | The proposed framework enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection. |