An Optimized Soft Computing Based Passage Retrieval System

In this paper we propose and evaluate a soft computing-based passage retrieval system for Question Answering Systems (QAS). Fuzzy PR, our base-line passage retrieval system, employs a similarity measure that attempts to model accurately the question reformulation intuition. The similarity measure includes fuzzy logic-based models that evaluate efficiently the proximity of question terms and detect term variations occurring within a passage. Our experimental results using FuzzyPR on the TREC and CLEF corpora show that our novel passage retrieval system achieves better performance compared to other similar systems. Finally, we describe the performance results of OptFuzzyPR, an optimized version of FuzzyPR, created by optimizing the values of FuzzyPR system parameters using genetic algorithms

Building a dialogue system for question-answer forum websites

[EU] Dialogo-sistemak gizakiak laguntzeko sistema automatikoak dira, eta beren ezaugarri nagusia da komunikazioa hizkuntza naturalaren bidez gauzatzeko gai direla. Azken boladan bultzada handia jaso eta eguneroko tresnetan aurkitu daitezke (Siri, Cortana, Alexa, etab.). Sistema hauen erabilera handitu ahala, Community Question Answering (CQA) edo Frequently Asked Questions (FAQ) direlakoak dialogo bitartez atzitzeko interesa zeharo handitu da, bereziki enpresa munduan. Egungo dialogo sistemen elkarrizketarako ahalmena, ordea, oso mugatua da, eskuzko erregelen bidez definituta baitaude. Horrek domeinu berri batean ezartzeko edo behin produkzioan martxan dagoenean monitorizatu eta egokitzeko kostuak handitzen ditu. Bestalde, nahiz eta ikaskuntza sakona bezalako teknikek oso emaitza onak lortu dituzten Hizkuntzaren Prozesamenduko alor desberdinetan, asko sufritzen dute datu eskasiaren arazoa, datu kopuru izugarriak behar baitituzte ikasketarako. Hemen aurkeztutako proiektuaren helburu nagusia aipatutako mugak arintzea da, sare neuronaletan oinarritutako sistema bat inplementatuz eta sistema hauen etorkizuneko garapena bultzatu eta errazteko CQA datu multzo bat sortuz.[EN] Dialogue-systems are automatic systems developed for helping humans in their daily routines. The main characteristic of these systems is that they are able to communicate using natural language. Lately, dialogue agents are becoming increasingly trendy and are already part of our lives as they are implemented in many tools (Siri, Cortana, Alexa...). This incursion of voice agents has increased the interest of accessing Community Question Answering (CQA) and Frequently Asked Questions (FAQ) information by dialogue means, specially in the industrial world. Nowadays, dialogue systems have their conversational ability very limited as they are de ned by hand-crafted rules. This hand-crafted nature, makes domain adaptation an extremely costly and time consuming task. On the other hand, deep learning based techniques, that have achieved state-of-the-art results in many Natural Language Processing (NLP) tasks, sufer from lack of data as they need huge amounts of labelled records for training. So, the main aim of this project, is to develop a neural system together with a CQA dataset for enabling future research in CQA dialogue systems

Inferences at encoding vs. retrieval: Clarifying the issues based on a developmental perspective

This study addressed the controversy surrounding the locus of the inferential process (encoding vs. retrieval) in story comprehension by adopting a developmental perspective. Second, fifth, and eighth grade children, and college undergraduates, read eight stories from which two types of inferences could be drawn. Bridging inferences are inferences critical to the comprehension of a story while forward inferences are not. Eight questions (four inference and four filler) were answered to each story, and the dependent variables of reaction time and error rate were measured. The hypothesis that bridging inferences would be drawn at encoding was clearly supported as was the corollary that forward inferences would not be drawn until retrieval. Additionally, the hypothesis that second grade children would successfully draw the bridging inferences was supported, contradicting much previous research. Errors reached asymptotic level at the fifth grade while reaction time decreased until the eighth grade, after which there were no significant differences. Bridging inference questions were answered faster and more accurately than forward inference questions, at all grade levels

Desing, Development and Benchmarking of Algorithms for Conversational Search

Developing an intelligent dialog system that not only emulates human conversation, but also answers to difficult topics is one of the most important fields on several research area. In recent years, great strides have been made in this area and several companies and research groups create competitions that aim to find solutions to problems like Conversational Information Seeking and Natural Language Generation. On thisworkwe see one of them in particular: TREC CaST (Conversational Assistance Track). We analyze several techniques that allowto create a conversational system and how we can improve the results by using neural techniques. On this work we examine how to retrieve relevant documents by using Lucene and then to re-rank this documents by using a neural text-classifier like BERT.Developing an intelligent dialog system that not only emulates human conversation, but also answers to difficult topics is one of the most important fields on several research area. In recent years, great strides have been made in this area and several companies and research groups create competitions that aim to find solutions to problems like Conversational Information Seeking and Natural Language Generation. On thisworkwe see one of them in particular: TREC CaST (Conversational Assistance Track). We analyze several techniques that allowto create a conversational system and how we can improve the results by using neural techniques. On this work we examine how to retrieve relevant documents by using Lucene and then to re-rank this documents by using a neural text-classifier like BERT

Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents--or short passages--in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms--such as a person's name or a product model number--not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections--such as the document index of a commercial Web search engine--containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks.Comment: PhD thesis, Univ College London (2020