Search Results for author:
Found 30 papers, 15 papers with code
Keyphrase Prediction from Video Transcripts: New Dataset and Directions
To this end, this work studies new challenges of KP in transcripts of videos, an understudied domain for KP that involves informal texts and non-cohesive presentation styles.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation
Additionally, we propose minimizing class-aware Higher-order Moment Matching (HMM) to align the corresponding class regions on the source and target domains.
Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification
Then we merge the sentences selected for a specific aspect as the input for the summarizer to produce the aspect-based summary.
NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation
In this paper, we propose a novel Noisy Layer Generation method (NAYER) which relocates the random source from the input to a noisy layer and utilizes the meaningful constant label-text embedding (LTE) as the input.
FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing
Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval.
Ranked #2 on
Human Judgment Correlation
on Flickr8k-Expert
Class based Influence Functions for Error Detection
Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets.
Vision Transformer Visualization: What Neurons Tell and How Neurons Behave?
Our approach departs from the computational process of ViTs with a focus on visualizing the local and global information in input images and the latent feature embeddings at multiple levels.
An Additive Instance-Wise Approach to Multi-class Model Interpretation
A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner.
ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection
Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks.
STEM: An Approach to Multi-Source Domain Adaptation With Guarantees
To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor.
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar.
TIMERS: Document-level Temporal Relation Extraction
We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language.
Ranked #3 on
Temporal Relation Classification
on TB-Dense
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference
It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text.
MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning
To this end, we propose in this paper a novel model for multi-source DA using the theory of optimal transport and imitation learning.
Imitation Learning
Multi-Source Unsupervised Domain Adaptation
+1
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution
Event coreference resolution is an important research problem with many applications.
STEM: An Approach to Multi-Source Domain Adaptation With Guarantees
To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation
The proposed model outperforms the state-of-the-art models on the new AD dataset, providing a strong baseline for future research on this dataset.
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation
In addition, we propose a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents
Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction.
Scene Graph Modification Based on Natural Language Commands
In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user's command.
A Simple but Effective BERT Model for Dialog State Tracking on Resource-Limited Systems
In a task-oriented dialog system, the goal of dialog state tracking (DST) is to monitor the state of the conversation from the dialog history.
Ranked #4 on
Dialogue State Tracking
on Wizard-of-Oz
A Gated Self-attention Memory Network for Answer Selection
Answer selection is an important research problem, with applications in many areas.
Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation
In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i. e. human transcriptions, instead of Automatic Speech Recognition (ASR)'s transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
The Context-Dependent Additive Recurrent Neural Net
Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP).
Preserving Distributional Information in Dialogue Act Classification
This paper introduces a novel training/decoding strategy for sequence labeling.
A Generative Attentional Neural Network Model for Dialogue Act Classification
We propose a novel generative neural network architecture for Dialogue Act classification.
A Hierarchical Neural Model for Learning Sequences of Dialogue Acts
We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs).
