Search Results for author:
Found 13 papers, 6 papers with code
Fancy Man Launches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction
Automatic or semi-automatic conversion of protocols specifying steps in performing a lab procedure into machine-readable format benefits biological research a lot.
Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?
We employ a probing technique to extract representations from different layers of the model and apply these to classification tasks.
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities.
Consistent and Asymptotically Statistically-Efficient Solution to Camera Motion Estimation
Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community.
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation.
Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts
Educational materials such as survey articles in specialized fields like computer science traditionally require tremendous expert inputs and are therefore expensive to create and update.
Large Language Models Are Partially Primed in Pronoun Interpretation
Recent psycholinguistic studies suggest that humans adapt their referential biases with recent exposure to referential patterns; closely replicating three relevant psycholinguistic experiments from Johnson & Arnold (2022) in an in-context learning (ICL) framework, we found that InstructGPT adapts its pronominal interpretations in response to the frequency of referential patterns in the local discourse, though in a limited fashion: adaptation was only observed relative to syntactic but not semantic biases.
GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost
Large pre-trained models have revolutionized natural language processing (NLP) research and applications, but high training costs and limited data resources have prevented their benefits from being shared equally amongst speakers of all the world's languages.
Filter and evolve: progressive pseudo label refining for semi-supervised automatic speech recognition
The model is iteratively updated to correct the unreliable pseudo labels to minimize the effect of noisy labels.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives
Irony is a ubiquitous figurative language in daily communication.
Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective
Accented speech recognition and accent classification are relatively under-explored research areas in speech technology.
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection
The past ten years have witnessed the rapid development of text-based intent detection, whose benchmark performances have already been taken to a remarkable level by deep learning techniques.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Fancy Man Lauches Zippo at WNUT 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction
Automatic or semi-automatic conversion of protocols specifying steps in performing a lab procedure into machine-readable format benefits biological research a lot.
