Lynda Tamine


Exploring the Value of Multi-View Learning for Session-Aware Query Representation
Diego Ortiz | Jose Moreno | Gilles Hubert | Karen Pinel-Sauvagnat | Lynda Tamine
Findings of the Association for Computational Linguistics: NAACL 2022

Recent years have witnessed a growing interest towards learning distributed query representations that are able to capture search intent semantics. Most existing approaches learn query embeddings using relevance supervision making them suited only to document ranking tasks. Besides, they generally consider either user’s query reformulations or system’s rankings whereas previous findings show that user’s query behavior and knowledge change depending on the system’s results, intertwine and affect each other during the completion of a search task. In this paper, we explore the value of multi-view learning for generic and unsupervised session-aware query representation learning. First, single-view query embeddings are obtained in separate spaces from query reformulations and document ranking representations using transformers. Then, we investigate the use of linear (CCA) and non linear (UMAP) multi-view learning methods, to align those spaces with the aim of revealing similarity traits in the multi-view shared space. Experimental evaluation is carried out in a query classification and session-based retrieval downstream tasks using respectively the KDD and TREC session datasets. The results show that multi-view learning is an effective and controllable approach for unsupervised learning of generic query representations and can reflect search behavior patterns.

Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?
Jesus Lovon-Melgarejo | Jose G. Moreno | Romaric Besançon | Olivier Ferret | Lynda Tamine
Proceedings of the 29th International Conference on Computational Linguistics

Despite the success of state-of-the-art pre-trained language models (PLMs) on a series of multi-hop reasoning tasks, they still suffer from their limited abilities to transfer learning from simple to complex tasks and vice-versa. We argue that one step forward to overcome this limitation is to better understand the behavioral trend of PLMs at each hop over the inference chain. Our critical underlying idea is to mimic human-style reasoning: we envision the multi-hop reasoning process as a sequence of explicit single-hop reasoning steps. To endow PLMs with incremental reasoning skills, we propose a set of inference strategies on relevant facts and distractors allowing us to build automatically generated training datasets. Using the SHINRA and ConceptNet resources jointly, we empirically show the effectiveness of our proposal on multiple-choice question answering and reading comprehension, with a relative improvement in terms of accuracy of 68.4% and 16.0% w.r.t. classic PLMs, respectively.


HeidelToul: A Baseline Approach for Cross-document Event Ordering
Bilel Moulahi | Jannik Strötgen | Michael Gertz | Lynda Tamine
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)