52 citations · 57 across the 9 of their papers we have counts for
14 papers
Enhancing Open-Vocabulary Object Detection through Multi-Level Fine-Grained Visual-Language Alignment
Tianyi Zhang, Antoine Simoulin, Kai Li +5
Traditional object detection systems are typically constrained to predefined categories, limiting their applicability in dynamic environments. In contrast, open-vocabulary object d…
Scaling Reinforcement Learning for Content Moderation with Large Language Models
Hamed Firooz, Rui Liu, Yuchen Lu +15
Content moderation at scale remains one of the most pressing challenges in today's digital ecosystem, where billions of user- and AI-generated artifacts must be continuously evalua…
The Automated Verification of Textual Claims (AVeriTeC) Shared Task
Michael Schlichtkrull, Yulong Chen, Chenxi Whitehouse +9
The Automated Verification of Textual Claims (AVeriTeC) shared task asks participants to retrieve evidence and predict veracity for real-world claims checked by fact-checkers. Evid…
Emotion-Cause Pair Extraction in Customer Reviews
Arpit Mittal, Jeel Tejaskumar Vaishnav, Aishwarya Kaliki +2
Emotion-Cause Pair Extraction (ECPE) is a complex yet popular area in Natural Language Processing due to its importance and potential applications in various domains. In this repor…
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
Rami Aly, Zhijiang Guo, Michael Schlichtkrull +5
Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation…
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets
Esma Balkir, Masha Naslidnyk, Dave Palfrey +1
Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottlen…
Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning
Daniele Bonadiman, Anjishnu Kumar, Arpit Mittal
The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve equivalent questions that result in the same answer as the original question. Such a system can be used to u…
Generating Token-Level Explanations for Natural Language Inference
James Thorne, Andreas Vlachos, Christos Christodoulopoulos +1
The task of Natural Language Inference (NLI) is widely modeled as supervised sentence pair classification. While there has been a lot of work recently on generating explanations of…
Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering
Fréderic Godin, Anjishnu Kumar, Arpit Mittal
In this paper, we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications. We examine the perform…
The Fact Extraction and VERification (FEVER) Shared Task
James Thorne, Andreas Vlachos, Oana Cocarascu +2
We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be…
Demand-Weighted Completeness Prediction for a Knowledge Base
Andrew Hopkinson, Amit Gurdasani, Dave Palfrey +1
In this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used. Defining an entit…
Simple Large-scale Relation Extraction from Unstructured Text
Christos Christodoulopoulos, Arpit Mittal
Knowledge-based question answering relies on the availability of facts, the majority of which cannot be found in structured sources (e.g. Wikipedia info-boxes, Wikidata). One of th…
FEVER: a large-scale dataset for Fact Extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos +1
In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generat…
Labeling Topics with Images using Neural Networks
Nikolaos Aletras, Arpit Mittal
Topics generated by topic models are usually represented by lists of terms or alternatively using short phrases and images. The current state-of-the-art work on labeling topics…