Search Results for author:
Found 17 papers, 6 papers with code
Factual Confidence of LLMs: on Reliability and Robustness of Current Estimators
Our experiments across a series of LLMs indicate that trained hidden-state probes provide the most reliable confidence estimates, albeit at the expense of requiring access to weights and training data.
Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search
This paper introduces the "Shopping Queries Dataset", a large dataset of difficult Amazon search queries and results, publicly released with the aim of fostering research in improving the quality of search results.
Tailoring and Evaluating the Wikipedia for in-Domain Comparable Corpora Extraction
Our best metric for domainness shows a strong correlation with the human-judged precision, representing a reasonable automatic alternative to assess the quality of domain-specific corpora.
A Context-Aware Approach for Detecting Check-Worthy Claims in Political Debates
In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking.
Machine Translation Evaluation Meets Community Question Answering
We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering.
SemEval-2016 Task 3: Community Question Answering
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic.
SemEval-2017 Task 3: Community Question Answering
We describe SemEval-2017 Task 3 on Community Question Answering.
SemEval-2015 Task 3: Answer Selection in Community Question Answering
Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e. g., the exploitation of the interaction between users and the structure of related posts.
Global Thread-Level Inference for Comment Classification in Community Question Answering
Community question answering, a recent evolution of question answering in the Web context, allows a user to quickly consult the opinion of a number of people on a particular topic, thus taking advantage of the wisdom of the crowd.
BookQA: Stories of Challenges and Opportunities
We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer.
It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction
We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates.
Automatic Fact-Checking Using Context and Discourse Information
We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information.
Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks
In this paper, we investigate the representations learned at different layers of NMT encoders.
Machine Translation Evaluation with Neural Networks
We present a framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation.
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation.
Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering
We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language.
