Papers with context-aware systems

3 papers
PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding (2022.emnlp-industry)

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Challenge: In a large fraction of the global traffic from smart digital assistants, frictions in dialogues may be attributed to incorrect understanding of the entities in a user's query due to factors including ambiguous mentions, mispronunciation, background noise and faulty on-device signal processing.
Approach: They propose a parametric transformer-based language model to learn patterns from in-session customer-device interactions coupled with a non-parametric personalized entity index to compute the correct query.
Outcome: The proposed system improves on the existing system and shows that it can learn the correct query from in-session customer-device interactions.
Recent Advances in Online Hate Speech Moderation: Multimodality and the Role of Large Models (2024.findings-emnlp)

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Challenge: HS is any communication demeaning a person or a group based on social or ethnic characteristics that undermines social harmony and individual safety . the recent Israel-Hamas conflict has escalated both anti-Muslim and anti-Semitic sentiments worldwide .
Approach: They examine the role of large language models and large multimodal models in HS moderation . they examine how text, images, and audio interact to spread hate speech .
Outcome: The findings highlight the need for solutions in low-resource settings and highlight the gaps in existing methods.
TANDO: A Corpus for Document-level Machine Translation (2022.lrec-1)

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Challenge: Document-level Neural Machine Translation aims to increase the quality of neural translation models by taking into account contextual information.
Approach: They propose to use document-level corpus for Basque-Spanish language pairs to take into account contextual information and perform fine-grained evaluations of gender and gender.
Outcome: The proposed corpus is suitable for fine-grained evaluation of document-level machine translation systems.

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