Neural retrieval models have superseded classic bag-of-words methods such as BM25 as the retrieval framework of choice. However, neural systems lack the interpretability of bag-of-words models; it is not trivial to connect a query change to a change in the latent space that ultimately determines the retrieval results. To shed light on this embedding space, we learn a “query decoder” that, given a latent representation of a neural search engine, generates the corresponding query. We show that it is possible to decode a meaningful query from its latent representation and, when moving in the right direction in latent space, to decode a query that retrieves the relevant paragraph. In particular, the query decoder can be useful to understand “what should have been asked” to retrieve a particular paragraph from the collection. We employ the query decoder to generate a large synthetic dataset of query reformulations for MSMarco, leading to improved retrieval performance. On this data, we train a pseudo-relevance feedback (PRF) T5 model for the application of query suggestion that outperforms both query reformulation and PRF information retrieval baselines.

This paper describes SW1, the first version of a semantically annotated snapshot of the English Wikipedia. In recent years Wikipedia has become a valuable resource for both the Natural Language Processing (NLP) community and the Information Retrieval (IR) community. Although NLP technology for processing Wikipedia already exists, not all researchers and developers have the computational resources to process such a volume of information. Moreover, the use of different versions of Wikipedia processed differently might make it difficult to compare results. The aim of this work is to provide easy access to syntactic and semantic annotations for researchers of both NLP and IR communities by building a reference corpus to homogenize experiments and make results comparable. These resources, a semantically annotated corpus and a “entity containment” derived graph, are licensed under the GNU Free Documentation License and available from http://www.yr-bcn.es/semanticWikipedia

In this paper we present the procedure we followed to develop the Italian Super Sense Tagger. In particular, we adapted the English SuperSense Tagger to the Italian Language by exploiting a parallel sense labeled corpus for training. As for English, the Italian tagger uses a fixed set of 26 semantic labels, called supersenses, achieving a slightly lower accuracy due to the lower quality of the Italian training data. Both taggers accomplish the same task of identifying entities and concepts belonging to a common set of ontological types. This parallelism allows us to define effective methodologies for a broad range of cross-language knowledge acquisition tasks