Papers by Abhishek Agrawal

4 papers
Unified Semantic Parsing with Weak Supervision (P19-1)

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Challenge: Semantic parsing over multiple knowledge bases requires high-quality annotations of (utterance, program) pairs.
Approach: They propose a framework to build a unified multi-domain enabled semantic parser with weak supervision.
Outcome: The proposed model improves performance by 20% on the Overnight dataset.
CHICA: A Developmental Corpus of Child-Caregiver’s Face-to-face vs. Video Call Conversations in Middle Childhood (2024.lrec-main)

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Challenge: Existing studies of language-in-interaction focus on the two ends of the developmental spectrum, i.e., early childhood and adulthood, leaving a gap in our knowledge about how development unfolds, especially across middle childhood.
Approach: They propose to use CHICA to analyze child-caregiver conversations at home . they use mobile, lightweight eye-tracking and head motion detection to optimize the naturalness of the recordings.
Outcome: The proposed corpus of child-caregiver conversations at home was compared with a previous corpus based on a set of conversations between children aged 7, 9, and 11 years old.
Automatic Coding of Contingency in Child-Caregiver Conversations (2024.lrec-main)

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Challenge: Current research on children's language development relies on manual annotation of a small sample of children, which limits our ability to draw general conclusions about development.
Approach: They propose to use automatic tools to assess contingency in children's natural interactions with caregivers by annotating a small set of data with a Transformer-based model.
Outcome: The proposed model replicates existing results and generates new data-driven hypotheses.
Automatic Annotation of Grammaticality in Child-Caregiver Conversations (2024.lrec-main)

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Challenge: Existing methods for analyzing child language acquisition have been tedious and inconsistent.
Approach: They propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate 4,000 utterances from a large corpus of transcribed conversations.
Outcome: The proposed method achieves human inter-annotation agreement levels and is faster and reproducible than manual methods.

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