Search Results for author:
Found 46 papers, 17 papers with code
A Transformer-based Threshold-Free Framework for Multi-Intent NLU
Furthermore, we propose a threshold-free intent multi-intent classifier that utilizes the output of IND task and detects the multiple intents without depending on the threshold.
Structure-aware Fine-tuning for Code Pre-trained Models
Over the past few years, we have witnessed remarkable advancements in Code Pre-trained Models (CodePTMs).
Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability
This study examines the decoy effect's underexplored influence on user search interactions and methods for measuring information retrieval (IR) systems' vulnerability to this effect.
Apollo: An Lightweight Multilingual Medical LLM towards Democratizing Medical AI to 6B People
Despite the vast repository of global medical knowledge predominantly being in English, local languages are crucial for delivering tailored healthcare services, particularly in areas with limited medical resources.
GraphWiz: An Instruction-Following Language Model for Graph Problems
Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored.
Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations
Existing retrieval-based methods have made significant strides in maintaining long-term conversations.
From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting
IMP-TIP follows the ``From Good to Great" concept, collecting multiple potential solutions from both LLMs and their Tool-Augmented counterparts for the same math problem, and then selecting or re-generating the most accurate answer after cross-checking these solutions via tool-augmented interleaf prompting.
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers
This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing.
Light Field Imaging in the Restrictive Object Space based on Flexible Angular Plane
The light field imaging in restrictive object space (ROS-LF) is complicated but significant.
Decoy Effect in Search Interaction: A Pilot Study
In recent years, the influence of cognitive effects and biases on users' thinking, behaving, and decision-making has garnered increasing attention in the field of interactive information retrieval.
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
This indicates that crafting multilingual corpora can be regarded as a vital strategy for enhancing model performance in a specific language, especially in mathematical reasoning tasks.
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding
The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios.
A Meta-Evaluation of C/W/L/A Metrics: System Ranking Similarity, System Ranking Consistency and Discriminative Power
In this study, we investigate the statistical stability of C/W/L/A metrics from the perspective of: (1) the system ranking similarity among aggregations, (2) the system ranking consistency of aggregations and (3) the discriminative power of aggregations.
SSCBench: Monocular 3D Semantic Scene Completion Benchmark in Street Views
Monocular scene understanding is a foundational component of autonomous systems.
3D Semantic Scene Completion
3D Semantic Scene Completion from a single 2D image
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
To mitigate this brittleness, we propose a novel Chain-of-Knowledge (CoK) prompting, where we aim at eliciting LLMs to generate explicit pieces of knowledge evidence in the form of structure triple.
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
To tackle the issue, in this paper, we present TransCoder, a unified Transferable fine-tuning strategy for Code representation learning.
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario
In this paper, we introduce gradient descent into black-box tuning scenario through knowledge distillation.
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives
Large language models (LLMs) have shown increasing power on various natural language processing (NLP) tasks.
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Personalized content-based recommender systems have become indispensable tools for users to navigate through the vast amount of content available on platforms like daily news websites and book recommendation services.
Alleviating Over-smoothing for Unsupervised Sentence Representation
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities.
Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision
Interestingly, during the training phase supervised by point labels, we discover that CNNs first learn to segment a cluster of pixels near the targets, and then gradually converge to predict groundtruth point labels.
Improve Retrieval-based Dialogue System via Syntax-Informed Attention
Since incorporating syntactic information like dependency structures into neural models can promote a better understanding of the sentences, such a method has been widely used in NLP tasks.
HugNLP: A Unified and Comprehensive Library for Natural Language Processing
In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is designed for NLP researchers to easily utilize off-the-shelf algorithms and develop novel methods with user-defined models and tasks in real-world scenarios.
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehension
Thus, model's comprehension ability towards real scenarios are hard to evaluate reasonably.
FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering
Despite the promising result of recent KAQA systems which tend to integrate linguistic knowledge from pre-trained language models (PLM) and factual knowledge from knowledge graphs (KG) to answer complex questions, a bottleneck exists in effectively fusing the representations from PLMs and KGs because of (i) the semantic and distributional gaps between them, and (ii) the difficulties in joint reasoning over the provided knowledge from both modalities.
Natural Response Generation for Chinese Reading Comprehension
To this end, we construct a new dataset called Penguin to promote the research of MRC, providing a training and test bed for natural response generation to real scenarios.
Chinese Reading Comprehension
Machine Reading Comprehension
+1
Bridge the Gap between Language models and Tabular Understanding
For instance, TPLMs jointly pre-trained with table and text input could be effective for tasks also with table-text joint input like table question answering, but it may fail for tasks with only tables or text as input such as table retrieval.
Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax
To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph.
Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters
In this paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to advance the study of dialogue agents and character alignment.
Corrected Evaluation Results of the NTCIR WWW-2, WWW-3, and WWW-4 English Subtasks
The noise is due to a fatal bug in the backend of our relevance assessment interface.
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure
To interpret these models, some probing methods have been applied.
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension
To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the coarse-grained representations of the source sequences, i. e., passage and question.
Automatic Prosody Annotation with Pre-Trained Text-Speech Model
Prosodic boundary plays an important role in text-to-speech synthesis (TTS) in terms of naturalness and readability.
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model
To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations.
Ranked #1 on
Spoken Language Understanding
on Spoken-SQuAD
Detecting Recolored Image by Spatial Correlation
In this paper, we try to explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring.
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling
Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge from a high-resource language to low-resource languages.
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension
Cross-lingual Machine Reading Comprehension (xMRC) is challenging due to the lack of training data in low-resource languages.
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering
In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage.
Towards Visual Explainable Active Learning for Zero-Shot Classification
This paper proposes a visual explainable active learning approach with its design and implementation called semantic navigator to solve the above problems.
Text Anchor Based Metric Learning for Small-footprint Keyword Spotting
Recently proposed metric learning approaches improved the generalizability of models for the KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts (SOTA) in terms of model size.
Self-supervised Dialogue Learning for Spoken Conversational Question Answering
We also utilize the proposed self-supervised learning tasks to capture intra-sentence coherence.
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension
To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the \textit{coarse-grained} representations of the source sequences, i. e., passage and question.
Impact of Low-Resolution ADC on DOA Estimation Performance for Massive MIMO Receive Array
In this paper, we present a new scenario of direction of arrival (DOA) estimation using massive multiple-input multiple-output (MIMO) receive array with low-resolution analog-to-digital convertors (ADCs), which can strike a good balance between performance and circuit cost.
Information Theory Signal Processing Information Theory
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering
Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow given the speech utterances and text corpora.
Audio Signal Processing
Conversational Question Answering
+2
Knowledge Distillation for Improved Accuracy in Spoken Question Answering
However, the recent work shows that ASR systems generate highly noisy transcripts, which critically limit the capability of machine comprehension on the SQA task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Towards Data Distillation for End-to-end Spoken Conversational Question Answering
In spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
