Search Results for author:
Found 36 papers, 19 papers with code
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks
Language models (LMs) are indispensable tools for natural language processing tasks, but their vulnerability to adversarial attacks remains a concern.
ToXCL: A Unified Framework for Toxic Speech Detection and Explanation
This draws a unique need for unified frameworks that can effectively detect and explain implicit toxic speech.
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
Motivated by this insight, we developed a Poisoned Sample Identification Module (PSIM) leveraging PEFT, which identifies poisoned samples through confidence, providing robust defense against weight-poisoning backdoor attacks.
Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning
Our studies demonstrate that an attacker can manipulate the behavior of large language models by poisoning the demonstration context, without the need for fine-tuning the model.
READ-PVLA: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language Modeling
Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal language grounding and video-language summarization.
DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding
Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query.
Improving Multimodal Sentiment Analysis: Supervised Angular Margin-based Contrastive Learning for Enhanced Fusion Representation
The effectiveness of a model is heavily reliant on the quality of the fusion representation of multiple modalities in multimodal sentiment analysis.
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment
Language models have been supervised with both language-only objective and visual grounding in existing studies of visual-grounded language learning.
Exploring the Potential of Large Language Models in Computational Argumentation
As large language models have demonstrated strong abilities in understanding context and generating natural language, it is worthwhile to evaluate the performance of LLMs on various computational argumentation tasks.
Contrastive Chain-of-Thought Prompting
Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations, to guide the model to reason step-by-step while reducing reasoning mistakes.
Rethinking Negative Pairs in Code Search
In our proposed loss function, we apply three methods to estimate the weights of negative pairs and show that the vanilla InfoNCE loss is a special case of Soft-InfoNCE.
Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models
Our method does not require external triggers and ensures correct labeling of poisoned samples, improving the stealthy nature of the backdoor attack.
Towards Interpretable Federated Learning
Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data.
Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction
Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case's charge, law article and penalty term.
Textual Manifold-based Defense Against Natural Language Adversarial Examples
To the best of our knowledge, this is the first NLP defense that leverages the manifold structure against adversarial attacks.
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?
The fine-tuning of pre-trained language models has a great success in many NLP fields.
Improving Neural Cross-Lingual Summarization via Employing Optimal Transport Distance for Knowledge Distillation
Current state-of-the-art cross-lingual summarization models employ multi-task learning paradigm, which works on a shared vocabulary module and relies on the self-attention mechanism to attend among tokens in two languages.
Capturing Greater Context for Question Generation
Automatic question generation can benefit many applications ranging from dialogue systems to reading comprehension.
R2D2: Reuse & Reduce via Dynamic Weight Diffusion for Training Efficient NLP Models
We propose R2D2 layers, a new neural block for training efficient NLP models.
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks
Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient.
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens.
Recurrently Controlled Recurrent Networks
Recurrent neural networks (RNNs) such as long short-term memory and gated recurrent units are pivotal building blocks across a broad spectrum of sequence modeling problems.
Densely Connected Attention Propagation for Reading Comprehension
Secondly, the dense connectors in our network are learned via attention instead of standard residual skip-connectors.
Ranked #2 on
Question Answering
on NewsQA
Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences
This task enables many potential applications such as question answering and paraphrase identification.
Self-Attentive Neural Collaborative Filtering
This paper has been withdrawn as we discovered a bug in our tensorflow implementation that involved accidental mixing of vectors across batches.
Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction
Attention is typically used to select informative sub-phrases that are used for prediction.
Reasoning with Sarcasm by Reading In-between
Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit.
Multi-range Reasoning for Machine Comprehension
Similarly, we achieve competitive performance relative to AMANDA on the SearchQA benchmark and BiDAF on the NarrativeQA benchmark without using any LSTM/GRU layers.
Ranked #5 on
Question Answering
on RACE
Multi-Pointer Co-Attention Networks for Recommendation
Our model operates on a multi-hierarchical paradigm and is based on the intuition that not all reviews are created equal, i. e., only a select few are important.
Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference
Firstly, we introduce a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning.
Ranked #7 on
Natural Language Inference
on SciTail
Cross Temporal Recurrent Networks for Ranking Question Answer Pairs
This paper explores the idea of learning temporal gates for sequence pairs (question and answer), jointly influencing the learned representations in a pairwise manner.
SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring
Our new method proposes a new \textsc{SkipFlow} mechanism that models relationships between snapshots of the hidden representations of a long short-term memory (LSTM) network as it reads.
Ranked #4 on
Automated Essay Scoring
on ASAP
Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs
Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs.
Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
The dominant neural architectures in question answer retrieval are based on recurrent or convolutional encoders configured with complex word matching layers.
Ranked #1 on
Question Answering
on SemEvalCQA
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text
Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text.
Utilizing Temporal Information for Taxonomy Construction
Taxonomies play an important role in many applications by organizing domain knowledge into a hierarchy of {`}is-a{'} relations between terms.
