Papers by Antoine Tixier
Unsupervised Word Polysemy Quantification with Multiresolution Grids of Contextual Embeddings (2021.eacl-main)
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| Challenge: | a new method to quantify polysemy is based on basic geometry in the contextual embedding space . word sense annotation has always been one of the tasks with the lowest interannotator agreement . |
| Approach: | They propose a method to estimate polysemy based on simple geometry in contextual embedding space. |
| Outcome: | The proposed method is fully unsupervised and data-driven . it can be used to sample sentences with different senses at no extra cost . |
BARThez: a Skilled Pretrained French Sequence-to-Sequence Model (2021.emnlp-main)
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| Challenge: | Inductive transfer learning has taken the entire NLU field by storm, with models such as BERT and BART setting new state-of-the-art on countless tasks. |
| Approach: | They introduce a large-scale pretrained seq2seq model for French that is very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT. |
| Outcome: | The proposed model outperforms existing models on discriminative and generative tasks on a French summarization dataset. |
FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation (2022.acl-long)
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| Challenge: | Existing evaluation metrics are not reliable, but require significant computational resources. |
| Approach: | They propose a method to learn a fixed, low cost version of any expensive NLG metric while retaining most of its original performance. |
| Outcome: | The proposed approach retains most of the original performance while running faster and faster. |
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. |
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. |