Iulian Vlad Serban

Also published as: Iulian Serban


2021

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Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval
Devang Kulshreshtha | Robert Belfer | Iulian Vlad Serban | Siva Reddy
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6% top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.

2017

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Piecewise Latent Variables for Neural Variational Text Processing
Iulian Vlad Serban | Alexander G. Ororbia | Joelle Pineau | Aaron Courville
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.

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Piecewise Latent Variables for Neural Variational Text Processing
Iulian Vlad Serban | Alexander Ororbia II | Joelle Pineau | Aaron Courville
Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing

Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.

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Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
Ryan Lowe | Michael Noseworthy | Iulian Vlad Serban | Nicolas Angelard-Gontier | Yoshua Bengio | Joelle Pineau
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality (Liu et al., 2016). Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM)that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model’s predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue mod-els unseen during training, an important step for automatic dialogue evaluation.

2016

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On the Evaluation of Dialogue Systems with Next Utterance Classification
Ryan Lowe | Iulian Vlad Serban | Michael Noseworthy | Laurent Charlin | Joelle Pineau
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Iulian Vlad Serban | Alberto García-Durán | Caglar Gulcehre | Sungjin Ahn | Sarath Chandar | Aaron Courville | Yoshua Bengio
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
Chia-Wei Liu | Ryan Lowe | Iulian Serban | Mike Noseworthy | Laurent Charlin | Joelle Pineau
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
Ryan Lowe | Nissan Pow | Iulian Serban | Joelle Pineau
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue