Search Results for author:
Found 13 papers, 7 papers with code
Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature
In this paper we present a quantitative and qualitative analysis of the system.
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
In this position paper, we argue that the classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble.
Unsupervised Domain Adaption for Neural Information Retrieval
We focus on the BEIR benchmark, which includes test datasets from several domains with no training data, and explore two scenarios: zero-shot, where the supervised system is trained in a large out-of-domain dataset (MS-MARCO); and unsupervised domain adaptation, where, in addition to MS-MARCO, the system is fine-tuned in synthetic data from the target domain.
IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases
Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance.
Multilingual Named Entity Recognition
named-entity-recognition
+4
Improving Code Generation by Training with Natural Language Feedback
The potential for pre-trained large language models (LLMs) to use natural language feedback at inference time has been an exciting recent development.
Training Language Models with Language Feedback at Scale
Third, finetuning the language model to maximize the likelihood of the chosen refinement given the input.
Training Language Models with Language Feedback
We learn from language feedback on model outputs using a three-step learning algorithm.
Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning
The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility.
Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems
In this work, we introduce \emph{Spot The Bot}, a cost-efficient and robust evaluation framework that replaces human-bot conversations with conversations between bots.
DoQA - Accessing Domain-Specific FAQs via Conversational QA
We present DoQA, a dataset with 2, 437 dialogues and 10, 917 QA pairs.
DoQA -- Accessing Domain-Specific FAQs via Conversational QA
We present DoQA, a dataset with 2, 437 dialogues and 10, 917 QA pairs.
Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for Basque
Conversational Question Answering (CQA) systems meet user information needs by having conversations with them, where answers to the questions are retrieved from text.
Give your Text Representation Models some Love: the Case for Basque
This is suboptimal as, for many languages, the models have been trained on smaller (or lower quality) corpora.
