Papers by Ali Elkahky
Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing (2023.eacl-main)
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Akshat Shrivastava, Shrey Desai, Anchit Gupta, Ali Elkahky, Aleksandr Livshits, Alexander Zotov, Ahmed Aly
| Challenge: | Task-oriented semantic parsing models have achieved strong results in recent years, but they often face obstacles adapting to novel settings with distinct semantics and scarce data. |
| Approach: | They propose a scenario-based semantic parsing model which isolates coarse-grained and fine-grounded aspects of the task and solves them with off-the-shelf neural modules. |
| Outcome: | The proposed model outperforms previous approaches in high-resource, low-resourced, and multilingual settings, and is modular, differentiable, interpretable, and allows extra supervision from scenarios. |
A Challenge Set and Methods for Noun-Verb Ambiguity (D18-1)
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| Challenge: | English part-of-speech taggers make egregious errors related to noun-verb ambiguity, despite having achieved 97%+ accuracy on the WSJ Penn Treebank since 2002. |
| Approach: | They propose to use a WSJ dataset to identify 30,000 examples of noun-verb ambiguity . they find that english part-of-speech taggers make egregious errors related to nouns and verbs . |
| Outcome: | The proposed model improves on the WSJ Penn Treebank by 14% and 52% relative to the previous model. |
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. |
The Morpho-syntactic Annotation of Animacy for a Dependency Parser (L18-1)
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| Challenge: | Animacy is a feature found in nouns such as 'gender', 'number' and 'case' that improves parser accuracy. |
| Approach: | They propose an annotation scheme and parser results for the animacy feature in Russian and Arabic, morphologically rich languages, using the universal dependency framework. |
| Outcome: | The proposed scheme and parser improve on the animacy feature in Russian and Arabic, and the results show that the feature is more accurate than other features found in nouns, namely, 'gender', , and 'number' |
Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks (L18-1)
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| Challenge: | a new language-independent model for sentiment analysis is proposed for social media . a sentiment dictionary cannot list all the possible ways people can express their opinions . |
| Approach: | They propose a language-independent model for multi-class sentiment analysis using a neural network architecture. |
| Outcome: | The proposed model does not rely on language-specific features such as ontologies, dictionaries, or morphological or syntactic pre-processing. |