Post-Doctoral Position on Neural Models for Dialog Analysis

Matthieu Labeau, Gaël Guibon, Chloé Clavel

Place of work: Telecom Paris, Palaiseau (Paris outskirt), France

Starting date: from February 2023

Context: The post-doctoral fellow will be integrated in the social computing theme of the Signal, Statis-tics and Learning (S2A) team at Telecom Paris. Research activity will be supervised by Chloé Clavel, Matthieu Labeau, members of the team, and Gaël Guibon (University of Lorraine and LORIA laboratory).

Candidate profile: As a minimum requirement, the successful candidate should have:

• A PhD degree in one or more of the following areas: machine learning, natural language processing, computational linguistics, affective computing.

• Excellent programming skills (preferably in Python)

• Excellent command of English

How to apply: The application should be formatted as **a single PDF file** and should include:

• A complete and detailed curriculum vitae

• A cover letter

• The defense and PhD reports

• The contact of two referees

The PDF file should be sent to the three supervisors: Chloé Clavel, Gaël Guibon, Matthieu Labeau:

chloe.clavel@telecom-paris.fr, gael.guibon@univ-lorraine.fr, matthieu.labeau@telecom-paris.fr

Subject: Flexible and Adaptable Learning for Dialog Analysis.

Keywords: natural language processing, semi-supervised learning, few-shot learning, multi-task learning, robust learning, dialog analysis.

Description: Current research on dialog analysis encompasses a large array of (often related) classification tasks, where an output sequence of labels corresponds to an input sequence of utterances. However, the domain of the textual data, the nature of the labels, and their granularity may vary widely among tasks, while available data is often scarce. These issues are often addressed with methods coming from semi-supervised learning (Van Engelen and Hoos, 2020), few-shot learning (Guibon et al., 2021a), and more recently meta-learning, either separately (Guibon et al., 2021b) or together (Ma et al., 2022). However, existing solutions are often specific to a particular setting, dataset, and task, and have blind spots: for example, few-shot and meta-learning approaches are not designed to deal with label imbalance, while real-world data will rarely be balanced. We plan to work towards flexible approaches to dialog analysis by following one or several of these research directions – while keeping in mind that domain adaptation is often required in a few-shot setting:

• Few-shot learning for label imbalance and structured data: FSL is mostly used in cases where it is possible to enforce a balance in labels for the training samples. However, this is difficult to do with structured data representations such as dialogues (Guibon et al., 2021a).

• Few-shot learning with insufficient data: How to better exploit new labels at inference time in an FSL setting? How to best make use of available unlabeled data (semi-supervised learning) or supplementary resources (any kind of ontology, typology of emotions, etc.)? (Ren et al., 2018).

• Few-shot joint multi-task learning: how to better integrate joint learning of different tasks in a few-shot setting? A possible lead is to exploit the structure of the data to create substitute tasks, working towards a model easily adaptable to a new set of labels with very little supervision, through short, multi-task fine-tuning (Ye, Lin, and Ren, 2021).

• Calibrated few-shot learning: labels are often uncertain, and highly dependent on the context or the bias of the annotator; this should be reflected in models, whether through calibration (Guo et al., 2022) or soft-labeling.

References

Guibon, G.; Labeau, M.; Flamein, H.; Lefeuvre, L.; and Clavel, C. 2021a. Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic.

Guibon, G.; Labeau, M.; Flamein, H.; Lefeuvre, L.; and Clavel, C. 2021b. Meta-learning for Classifying Previously Unseen Data Sources into Previously Unseen Emotional Categories. In Proceedings of the 1st Workshop on Meta-Learning and Its Applications to Natural Language Processing, 76–89. Online:
Association for Computational Linguistics.

Guo, Y.; Du, R.; Li, X.; Xie, J.; Ma, Z.; and Dong, Y. 2022. Learning Calibrated Class Centers for Few-Shot Classification by Pair-Wise Similarity. IEEE Transactions on Image Processing, 31: 4543–4555.

Ma, T.; Jiang, H.; Wu, Q.; Zhao, T.; and Lin, C.-Y. 2022. Decomposed Meta-Learning for Few-Shot Named Entity Recognition. In Findings of the Association for Computational Linguistics: ACL 2022, 1584–1596.

Ren, M.; Triantafillou, E.; Ravi, S.; Snell, J.; Swersky, K.; Tenenbaum, J. B.; Larochelle, H.; and Zemel, R. S. 2018. Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676.

Van Engelen, J. E.; and Hoos, H. H. 2020. A survey on semi-supervised learning. Machine Learning, 109(2): 373–440.

Ye, Q.; Lin, B. Y.; and Ren, X. 2021. CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 7163–7189. Online and Punta Cana, Dominican Republic: Association for Computational Linguistics.