46 citations · 111 across the 40 of their papers we have counts for
51 papers
H2CE: Modeling Geo-Semantic Interactions for POI Reranking with Heterogeneous Two-Stage Cross-Encoders
Zhengwei Bai, Moreno D'Incà, Danielle Class +1
Point-of-Interest (POI) reranking in local search must model query-conditioned tradeoffs among lexical semantics, geospatial proximity, and numerical quality signals such as rating…
Rank-Deviation Quality: A Distance-Aware Metric for Multi-Answer Retrieval and Ranking Evaluation
Xiaokun Zhou, Alessandro Moschitti, Danielle Class
We introduce Rank-Deviation Quality (RDQ), an evaluation metric for retrieval and ranking systems that adapts to queries with varying numbers of reference items, from a single corr…
Improving Document Retrieval Coherence for Semantically Equivalent Queries
Stefano Campese, Alessandro Moschitti, Ivano Lauriola
Dense Retrieval (DR) models have proven to be effective for Document Retrieval and Information Grounding tasks. Usually, these models are trained and optimized for improving the re…
Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning
Hyundong Cho, Karishma Sharma, Nicolaas Jedema +4
Language models are aligned to the collective voice of many, resulting in generic outputs that do not align with specific users' styles. In this work, we present Trial-Error-Explai…
Speechworthy Instruction-tuned Language Models
Hyundong Cho, Nicolaas Jedema, Leonardo F. R. Ribeiro +5
Current instruction-tuned language models are exclusively trained with textual preference data and thus are often not aligned with the unique requirements of other modalities, such…
Datasets for Multilingual Answer Sentence Selection
Matteo Gabburo, Stefano Campese, Federico Agostini +1
Answer Sentence Selection (AS2) is a critical task for designing effective retrieval-based Question Answering (QA) systems. Most advancements in AS2 focus on English due to the sca…
Measuring Retrieval Complexity in Question Answering Systems
Matteo Gabburo, Nicolaas Paul Jedema, Siddhant Garg +2
In this paper, we investigate which questions are challenging for retrieval-based Question Answering (QA). We (i) propose retrieval complexity (RC), a novel metric conditioned on t…
SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References
Matteo Gabburo, Siddhant Garg, Rik Koncel Kedziorski +1
Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions. Recent works (AVA, BEM)…
Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages
Shivanshu Gupta, Yoshitomo Matsubara, Ankit Chadha +1
While impressive performance has been achieved on the task of Answer Sentence Selection (AS2) for English, the same does not hold for languages that lack large labeled datasets. In…
Context-Aware Transformer Pre-Training for Answer Sentence Selection
Luca Di Liello, Siddhant Garg, Alessandro Moschitti
Answer Sentence Selection (AS2) is a core component for building an accurate Question Answering pipeline. AS2 models rank a set of candidate sentences based on how likely they answ…
Learning Answer Generation using Supervision from Automatic Question Answering Evaluators
Matteo Gabburo, Siddhant Garg, Rik Koncel-Kedziorski +1
Recent studies show that sentence-level extractive QA, i.e., based on Answer Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA) models, which generate answers…
QUADRo: Dataset and Models for QUestion-Answer Database Retrieval
Stefano Campese, Ivano Lauriola, Alessandro Moschitti
An effective paradigm for building Automated Question Answering systems is the re-use of previously answered questions, e.g., for FAQs or forum applications. Given a database (DB)…
Effective Pre-Training Objectives for Transformer-based Autoencoders
Luca Di Liello, Matteo Gabburo, Alessandro Moschitti
In this paper, we study trade-offs between efficiency, cost and accuracy when pre-training Transformer encoders with different pre-training objectives. For this purpose, we analyze…
Knowledge Transfer from Answer Ranking to Answer Generation
Matteo Gabburo, Rik Koncel-Kedziorski, Siddhant Garg +2
Recent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (t…
Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
Luca Di Liello, Siddhant Garg, Luca Soldaini +1
An important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved…
Paragraph-based Transformer Pre-training for Multi-Sentence Inference
Luca Di Liello, Siddhant Garg, Luca Soldaini +1
Inference tasks such as answer sentence selection (AS2) or fact verification are typically solved by fine-tuning transformer-based models as individual sentence-pair classifiers. R…
DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection
Nic Jedema, Thuy Vu, Manish Gupta +1
While transformers demonstrate impressive performance on many knowledge intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs) remains limited, as shown on…
Question-Answer Sentence Graph for Joint Modeling Answer Selection
Roshni G. Iyer, Thuy Vu, Alessandro Moschitti +1
This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learnin…
In Situ Answer Sentence Selection at Web-scale
Zeyu Zhang, Thuy Vu, Alessandro Moschitti
Current answer sentence selection (AS2) applied in open-domain question answering (ODQA) selects answers by ranking a large set of possible candidates, i.e., sentences, extracted f…
Double Retrieval and Ranking for Accurate Question Answering
Zeyu Zhang, Thuy Vu, Alessandro Moschitti
Recent work has shown that an answer verification step introduced in Transformer-based answer selection models can significantly improve the state of the art in Question Answering.…
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems
Yoshitomo Matsubara, Luca Soldaini, Eric Lind +1
Large transformer models can highly improve Answer Sentence Selection (AS2) tasks, but their high computational costs prevent their use in many real-world applications. In this pap…
Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation
Benjamin Muller, Luca Soldaini, Rik Koncel-Kedziorski +2
Open-Domain Generative Question Answering has achieved impressive performance in English by combining document-level retrieval with answer generation. These approaches, which we re…
Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering
Siddhant Garg, Alessandro Moschitti
In this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them. This is…
Joint Models for Answer Verification in Question Answering Systems
Zeyu Zhang, Thuy Vu, Alessandro Moschitti
This paper studies joint models for selecting correct answer sentences among the top provided by answer sentence selection (AS2) modules, which are core components of retrieval…