Papers by Jean-Pierre Lorré
Weak Supervision for Learning Discourse Structure (D19-1)
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| Challenge: | a weak supervision approach is a promising tool for learning discourse structure for multi-party dialogue. |
| Approach: | They propose a data programming paradigm that allows a user to label training data using expert-composed heuristics and transform them into probability distributions of the class labels. |
| Outcome: | The proposed approach outperforms both deep learning and traditional ML approaches on the task of learning discourse structure for multi-party dialogue. |
Speaker-change Aware CRF for Dialogue Act Classification (2020.coling-main)
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| Challenge: | Recent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer. |
| Approach: | They propose to modify the CRF layer to take speaker-change into account and learn meaningful transition patterns conditioned on speaker-changing DA labels. |
| Outcome: | The proposed model outperforms the original model with wide margins for some DA labels. |
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization (P18-1)
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Guokan Shang, Wensi Ding, Zekun Zhang, Antoine Tixier, Polykarpos Meladianos, Michalis Vazirgiannis, Jean-Pierre Lorré
| Challenge: | a novel graph-based framework for abstractive meeting speech summarization is developed . instead of grammatical, well-segmented sentences, the input is made of often ill-formed and ungrammatically ungrammatized text fragments called utterances. |
| Approach: | They propose a graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on annotations. |
| Outcome: | The proposed framework improves on the state-of-the-art on the AMI and ICSI corpus. |
Data Programming for Learning Discourse Structure (P19-1)
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| Challenge: | Discourse structures are a relational semantic structure that convey causal, topical, argumentative relations or more generally coherence relations. |
| Approach: | They propose to use Snorkel to label training data using expert-composed heuristics and transform them into probability distributions of the class labels given to training candidates. |
| Outcome: | The proposed paradigm can be used for difficult tasks such as that of discourse attachment. |
Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding (2020.aacl-main)
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| Challenge: | Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances according to whether they can be jointly summarized by a common abstractive sentence. |
| Approach: | They propose a neural contextual utterance encoder with three types of self-attention mechanisms and train it using the siamese and triplet energy-based meta-architectures. |
| Outcome: | The proposed system outperforms multiple energy-based and non-energy based baselines on the AMI corpus. |