Papers by Eugene Kharitonov
Word-order Biases in Deep-agent Emergent Communication (P19-1)
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| Challenge: | a recent study examines the "natural" word-order constraints that constrain neural networks . we train models to communicate about paths in a simple gridworld . |
| Approach: | They propose to inoculate a notion of "effort" into neural networks to make their linguistic behavior more human-like. |
| Outcome: | The proposed models show a strong tendency to avoid redundancy and minimize long-distance dependencies. |
Compositionality and Generalization In Emergent Languages (2020.acl-main)
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| Challenge: | a new study examines whether emergent languages possess compositionality . compositionality is a core concept in linguistics, but linguists' definitions assume full knowledge of primitive expressions and their combination rules. |
| Approach: | They propose to use compositionality to combine expressions according to systematic rules to refer to composite concepts. |
| Outcome: | The proposed language has compositionality, but it is not generalized, the authors show . they show that the more compositional a language is, the more easily it will be picked up by new learners . |
EGG: a toolkit for research on Emergence of lanGuage in Games (D19-3)
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| Challenge: | Existing approaches to simulating language emergence among deep neural agents are challenging due to the discrete nature of communication. |
| Approach: | They propose a toolkit that greatly simplifies the implementation of emergent-language communication games. |
| Outcome: | The proposed toolkit simplifies the implementation of emergent-language communication games. |
Text-Free Prosody-Aware Generative Spoken Language Modeling (2022.acl-long)
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Eugene Kharitonov, Ann Lee, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Morgane Riviere, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu
| Challenge: | Experimental results show that generative spoken language models (LMs) are natural unsupervised multitask learners. |
| Approach: | They propose a prosody-aware generative spoken language model that uses discovered units to generate natural, meaningful, and coherent speech. |
| Outcome: | The proposed model can generate natural, meaningful, and coherent speech given a spoken prompt. |
MAD Speech: Measures of Acoustic Diversity of Speech (2025.naacl-long)
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| Challenge: | Recent advances in generative spoken language modeling have produced models that produce speech in a wide range of voices, prosody and recording conditions. |
| Approach: | They propose acoustic diversity metrics that measure voice, gender, emotion, accent, background noise and a priori known diversity preferences for each facet. |
| Outcome: | The proposed metrics show that they achieve stronger agreement with diversity than baselines. |
textless-lib: a Library for Textless Spoken Language Processing (2022.naacl-demo)
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Eugene Kharitonov, Jade Copet, Kushal Lakhotia, Tu Anh Nguyen, Paden Tomasello, Ann Lee, Ali Elkahky, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, Yossi Adi
| Challenge: | Textless spoken language processing is an exciting area of research that promises to extend applicability of the standard NLP toolset onto spoken language and languages with few or no textual resources. |
| Approach: | They introduce textless-lib, a PyTorch-based library that provides textless spoken language processing tools. |
| Outcome: | The proposed library significantly simplifies research in the textless setting and will be a handful for speech researchers and the NLP community at large. |
Generative Spoken Dialogue Language Modeling (2023.tacl-1)
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Tu Anh Nguyen, Eugene Kharitonov, Jade Copet, Yossi Adi, Wei-Ning Hsu, Ali Elkahky, Paden Tomasello, Robin Algayres, Benoît Sagot, Abdelrahman Mohamed, Emmanuel Dupoux
| Challenge: | dGSLM is the first “textless” model able to generate audio samples of naturalistic spoken dialogues. |
| Approach: | They propose a model that generates speech, laughter, and other paralinguistic signals in two channels simultaneously and reproduces more naturalistic turn taking compared to a text-based cascaded model. |
| Outcome: | The proposed model reproduces more naturalistic and fluid turn taking than a text-based cascaded model. |
Textless Speech Emotion Conversion using Discrete & Decomposed Representations (2022.emnlp-main)
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Felix Kreuk, Adam Polyak, Jade Copet, Eugene Kharitonov, Tu Anh Nguyen, Morgan Rivière, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, Yossi Adi
| Challenge: | Existing methods for modifying emotion of speech are difficult because emotion affects all levels simultaneously. |
| Approach: | They propose a method to convert a spoken language speech into a model of emotion . they use phonetic-content units, prosodic features, speaker, and emotion to modify the emotion a speech utterance has. |
| Outcome: | The proposed method beats text-based systems in terms of perceived emotion and audio quality. |
On Generative Spoken Language Modeling from Raw Audio (2021.tacl-1)
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Kushal Lakhotia, Eugene Kharitonov, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Benjamin Bolte, Tu-Anh Nguyen, Jade Copet, Alexei Baevski, Abdelrahman Mohamed, Emmanuel Dupoux
| Challenge: | Using a set of metrics to evaluate the learned representations, we aim to create a system that learns from natural interactions as infants learn their first language. |
| Approach: | They propose a task of learning acoustic and linguistic characteristics from raw audio and a set of metrics to evaluate the learned representations at acustic, linguistic and encoding levels. |
| Outcome: | The proposed models evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. |