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Found 11 papers, 5 papers with code
NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness
We propose a new benchmark NoFunEval to evaluate code LMs on non-functional requirements and simple classification instances for both functional and non-functional requirements.
Structured Case-based Reasoning for Inference-time Adaptation of Text-to-SQL parsers
To the best of our knowledge, we are the first to attempt inference-time adaptation of Text-to-SQL models, and harness trainable structured similarity between subqueries.
Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers
Text-to-SQL parsers typically struggle with databases unseen during the train time.
Bootstrapping Multilingual Semantic Parsers using Large Language Models
Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remains to be a key mechanism for training task-specific multilingual models.
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study
RelateLM uses transliteration to convert the unseen script of limited LRL text into the script of a Related Prominent Language (RPL) (Hindi in our case).
Teaching keyword spotters to spot new keywords with limited examples
Towards easily customizable KWS models, we present KeySEM (Keyword Speech EMbedding), a speech embedding model pre-trained on the task of recognizing a large number of keywords.
Error-driven Fixed-Budget ASR Personalization for Accented Speakers
We consider the task of personalizing ASR models while being constrained by a fixed budget on recording speaker-specific utterances.
What's in a Name? Are BERT Named Entity Representations just as Good for any other Name?
We evaluate named entity representations of BERT-based NLP models by investigating their robustness to replacements from the same typed class in the input.
Black-box Adaptation of ASR for Accented Speech
Accordingly, we propose a novel coupling of an open-source accent-tuned local model with the black-box service where the output from the service guides frame-level inference in the local model.
Learning from Rules Generalizing Labeled Exemplars
Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) the coupled rule-exemplar supervision is effective in denoising rules.
Parallel Iterative Edit Models for Local Sequence Transduction
We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC).
Ranked #16 on
Grammatical Error Correction
on CoNLL-2014 Shared Task
