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1
Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles
In: CtrlGen: Controllable Generative Modeling in Language and Vision ; https://hal.inria.fr/hal-03540084 ; CtrlGen: Controllable Generative Modeling in Language and Vision, Jan 2022, virtual, France (2022)
Abstract: International audience ; Linking neural representations to linguistic factors is crucial in order to build and analyze NLP models interpretable by humans. Among these factors, syntactic roles (e.g. subjects, direct objects,. .) and their realizations are essential markers since they can be understood as a decomposition of predicative structures and thus the meaning of sentences. Starting from a deep probabilistic generative model with attention, we measure the interaction between latent variables and realizations of syntactic roles, and show that it is possible to obtain, without supervision, representations of sentences where different syntactic roles correspond to clearly identified different latent variables. The probabilistic model we propose is an Attention-Driven Variational Autoencoder (ADVAE). Drawing inspiration from Transformer-based machine translation models, ADVAEs enable the analysis of the interactions between latent variables and input tokens through attention. We also develop an evaluation protocol to measure disentanglement with regard to the realizations of syntactic roles. This protocol is based on attention maxima for the encoder and on disturbing individual latent variables for the decoder. Our experiments on raw English text from the SNLI dataset show that i) disentanglement of syntactic roles can be induced without supervision, ii) ADVAE separates more syntactic roles than classical sequence VAEs, iii) realizations of syntactic roles can be separately modified in sentences by mere intervention on the associated latent variables. Our work constitutes a first step towards unsupervised controllable content generation. The code for our work is publicly available 1 .
Keyword: [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
URL: https://hal.inria.fr/hal-03540084
https://hal.inria.fr/hal-03540084/document
https://hal.inria.fr/hal-03540084/file/lqVcfT-CTRLGEN%20%287%29.pdf
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2
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
In: Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03527328 ; Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021), Jan 2022, punta cana, Dominican Republic ; https://aclanthology.org/2021.wnut-1.47/ (2022)
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3
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
In: https://hal.inria.fr/hal-03161685 ; 2021 (2021)
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4
Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi
In: https://hal.inria.fr/hal-03161677 ; 2021 (2021)
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5
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
In: EACL 2021 - The 16th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03239087 ; EACL 2021 - The 16th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2021, Kyiv / Virtual, Ukraine ; https://2021.eacl.org/ (2021)
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6
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
In: NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ; https://hal.inria.fr/hal-03251105 ; NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jun 2021, Mexico City, Mexico (2021)
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7
PAGnol: An Extra-Large French Generative Model
In: https://hal.inria.fr/hal-03540159 ; [Research Report] LightON. 2021 (2021)
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8
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
In: https://hal.inria.fr/hal-03109187 ; 2021 (2021)
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9
Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models
In: W-NUT 2021 - 7th Workshop on Noisy User-generated Text (colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03540174 ; W-NUT 2021 - 7th Workshop on Noisy User-generated Text (colocated with EMNLP 2021), Association for computational linguistics, Nov 2021, Punta Cana, Dominican Republic (2021)
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10
Understanding the Impact of UGC Specificities on Translation Quality
In: W-NUT 2021 - Seventh Workshop on Noisy User-generated Text (colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03540175 ; W-NUT 2021 - Seventh Workshop on Noisy User-generated Text (colocated with EMNLP 2021), association for computational linguistics, Nov 2021, Punta Cana, Dominican Republic (2021)
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11
Challenging the Semi-Supervised VAE Framework for Text Classification
In: Second Workshop on Insights from Negative Results in NLP (colocated with EMNLP) ; https://hal.inria.fr/hal-03540081 ; Second Workshop on Insights from Negative Results in NLP (colocated with EMNLP), Nov 2021, Punta Cana, Dominican Republic ; https://insights-workshop.github.io/2021/ (2021)
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12
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889804 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, Canada. ⟨10.18653/v1/2020.acl-main.107⟩ (2020)
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13
CamemBERT: a Tasty French Language Model
In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889805 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States. ⟨10.18653/v1/2020.acl-main.645⟩ (2020)
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14
Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora
In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03161637 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States. pp.538-555, ⟨10.18653/v1/2020.acl-main.51⟩ (2020)
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15
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
In: https://hal.inria.fr/hal-03109106 ; 2020 (2020)
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16
Unsupervised Learning for Handling Code-Mixed Data: A Case Study on POS Tagging of North-African Arabizi Dialect
In: EurNLP - First annual EurNLP ; https://hal.archives-ouvertes.fr/hal-02270527 ; EurNLP - First annual EurNLP, Oct 2019, Londres, United Kingdom (2019)
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17
CamemBERT: a Tasty French Language Model
In: https://hal.inria.fr/hal-02445946 ; 2019 (2019)
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18
Enhancing BERT for Lexical Normalization
In: The 5th Workshop on Noisy User-generated Text (W-NUT) ; https://hal.inria.fr/hal-02294316 ; The 5th Workshop on Noisy User-generated Text (W-NUT), Nov 2019, Hong Kong, China (2019)
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19
What does BERT learn about the structure of language?
In: ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02131630 ; ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Jul 2019, Florence, Italy (2019)
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20
Contextualized Diachronic Word Representations
In: 1st International Workshop on Computational Approaches to Historical Language Change 2019 (colocated with ACL 2019) ; https://hal.archives-ouvertes.fr/hal-02194763 ; 1st International Workshop on Computational Approaches to Historical Language Change 2019 (colocated with ACL 2019), Aug 2019, Florence, Italy (2019)
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