Defining Textual Entailment

Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of text-based tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation. Much ingenuity has been devoted to developing algorithms for identifying textual entailments, but relatively little to saying what textual entailment actually is. This article is a review of the logical and philosophical issues involved in providing an adequate definition of textual entailment. We show that many natural definitions of textual entailment are refuted by counterexamples, including the most widely cited definition of Dagan et al. We then articulate and defend the following revised definition: T textually entails H = df typically, a human reading T would be justified in inferring the proposition expressed by H from the proposition expressed by T. We also show that textual entailment is context-sensitive, nontransitive, and nonmonotonic

Kas kontsiliatsionism on problemaatilises seoses unikaalsusega?

In my thesis, I defend Conciliationism from an objection first articulated by Kelly (2010). He argues that Conciliationism is not compatible with Permissivism, and, for this reason, Conciliationism commits one to the Uniqueness Thesis (Uniqueness). Kelly sees this as a significant liability, because, as he argues, Uniqueness amounts to an extremely strong and unobvious view. I show that Kelly’s argument is ambiguous between two incompatible versions of Permissivism: Options Permissivism and Background-Relative Permissivism. When Permissivism in understood in the former way, Kelly, in fact, establishes that Conciliationism implies a version Uniqueness. However, this version of Uniqueness is a moderate and plausible position. The other reading of Permissivism will be compatible with Conciliationism and will not imply Uniqueness. I conclude that Kelly’s argument fails to pose a problem for Conciliationism

Replies to Critics

UIDB/00183/2020 UIDP/00183/2020This text brings together replies to three commentaries on my Description of Situations: An Essay in Contextualist Epistemology (Springer, 2018) written by Modesto Gómez-Alonso, Anna Boncompagni and Marcin Lewiński.publishersversionpublishe

Enhancing scene text recognition with visual context information

This thesis addresses the problem of improving text spotting systems, which aim to detect and recognize text in unrestricted images (e.g. a street sign, an advertisement, a bus destination, etc.). The goal is to improve the performance of off-the-shelf vision systems by exploiting the semantic information derived from the image itself. The rationale is that knowing the content of the image or the visual context can help to decide which words are the correct andidate words. For example, the fact that an image shows a coffee shop makes it more likely that a word on a signboard reads as Dunkin and not unkind. We address this problem by drawing on successful developments in natural language processing and machine learning, in particular, learning to re-rank and neural networks, to present post-process frameworks that improve state-of-the-art text spotting systems without the need for costly data-driven re-training or tuning procedures. Discovering the degree of semantic relatedness of candidate words and their image context is a task related to assessing the semantic similarity between words or text fragments. However, semantic relatedness is more general than similarity (e.g. car, road, and traffic light are related but not similar) and requires certain adaptations. To meet the requirements of these broader perspectives of semantic similarity, we develop two approaches to learn the semantic related-ness of the spotted word and its environmental context: word-to-word (object) or word-to-sentence (caption). In the word-to-word approach, word embed-ding based re-rankers are developed. The re-ranker takes the words from the text spotting baseline and re-ranks them based on the visual context from the object classifier. For the second, an end-to-end neural approach is designed to drive image description (caption) at the sentence-level as well as the word-level (objects) and re-rank them based not only on the visual context but also on the co-occurrence between them. As an additional contribution, to meet the requirements of data-driven ap-proaches such as neural networks, we propose a visual context dataset for this task, in which the publicly available COCO-text dataset [Veit et al. 2016] has been extended with information about the scene (including the objects and places appearing in the image) to enable researchers to include the semantic relations between texts and scene in their Text Spotting systems, and to offer a common evaluation baseline for such approaches.Aquesta tesi aborda el problema de millorar els sistemes de reconeixement de text, que permeten detectar i reconèixer text en imatges no restringides (per exemple, un cartell al carrer, un anunci, una destinació d’autobús, etc.). L’objectiu és millorar el rendiment dels sistemes de visió existents explotant la informació semàntica derivada de la pròpia imatge. La idea principal és que conèixer el contingut de la imatge o el context visual en el que un text apareix, pot ajudar a decidir quines són les paraules correctes. Per exemple, el fet que una imatge mostri una cafeteria fa que sigui més probable que una paraula en un rètol es llegeixi com a Dunkin que no pas com unkind. Abordem aquest problema recorrent a avenços en el processament del llenguatge natural i l’aprenentatge automàtic, en particular, aprenent re-rankers i xarxes neuronals, per presentar solucions de postprocés que milloren els sistemes de l’estat de l’art de reconeixement de text, sense necessitat de costosos procediments de reentrenament o afinació que requereixin grans quantitats de dades. Descobrir el grau de relació semàntica entre les paraules candidates i el seu context d’imatge és una tasca relacionada amb l’avaluació de la semblança semàntica entre paraules o fragments de text. Tanmateix, determinar l’existència d’una relació semàntica és una tasca més general que avaluar la semblança (per exemple, cotxe, carretera i semàfor estan relacionats però no són similars) i per tant els mètodes existents requereixen certes adaptacions. Per satisfer els requisits d’aquestes perspectives més àmplies de relació semàntica, desenvolupem dos enfocaments per aprendre la relació semàntica de la paraula reconeguda i el seu context: paraula-a-paraula (amb els objectes a la imatge) o paraula-a-frase (subtítol de la imatge). En l’enfocament de paraula-a-paraula s’usen re-rankers basats en word-embeddings. El re-ranker pren les paraules proposades pel sistema base i les torna a reordenar en funció del context visual proporcionat pel classificador d’objectes. Per al segon cas, s’ha dissenyat un enfocament neuronal d’extrem a extrem per explotar la descripció de la imatge (subtítol) tant a nivell de frase com a nivell de paraula i re-ordenar les paraules candidates basant-se tant en el context visual com en les co-ocurrències amb el subtítol. Com a contribució addicional, per satisfer els requisits dels enfocs basats en dades com ara les xarxes neuronals, presentem un conjunt de dades de contextos visuals per a aquesta tasca, en el què el conjunt de dades COCO-text disponible públicament [Veit et al. 2016] s’ha ampliat amb informació sobre l’escena (inclosos els objectes i els llocs que apareixen a la imatge) per permetre als investigadors incloure les relacions semàntiques entre textos i escena als seus sistemes de reconeixement de text, i oferir una base d’avaluació comuna per a aquests enfocaments

Leibnizian Bodies: Phenomena, Aggregates of Monads, or Both?

I propose a straightforward reconciliation of Leibniz’s conception of bodies as aggregates of simple substances (i.e., monads) with his doctrine that bodies are the phenomena of perceivers, without in the process saddling him with any equivocations. The reconciliation relies on the familiar idea that in Leibniz’s idiolect, an aggregate of Fs is that which immediately presupposes those Fs, or in other words, has those Fs as immediate requisites. But I take this idea in a new direction. Taking notice of the fact that Leibniz speaks of three respects in which one thing may immediately presuppose others--i.e., with respect to its being, its existence, and its reality--I argue that a phenomenon having its being in one perceiving substance (monad) can plausibly be understood to presuppose other perceiving substances (monads) in two of these respects. Accordingly, good sense can be made of both the claim that a phenomenon in one monad is an aggregate of other monads (in Leibniz’s technical sense of 'aggregate') and the (equivalent) claim that the latter monads are constituents of the phenomenon (in his technical sense of 'constituent'). So understood, the two conceptions of body are perfectly compatible, just as Leibniz seems to think

RecallM: An Architecture for Temporal Context Understanding and Question Answering

The ideal long-term memory mechanism for Large Language Model (LLM) based chatbots, would lay the foundation for continual learning, complex reasoning and allow sequential and temporal dependencies to be learnt. Creating this type of memory mechanism is an extremely challenging problem. In this paper we explore different methods of achieving the effect of long-term memory. We propose a new architecture focused on creating adaptable and updatable long-term memory for AGI systems. We demonstrate through various experiments the benefits of the RecallM architecture, particularly the improved temporal understanding of knowledge it provides.Comment: 12 pages, 6 figures, Our code is publicly available online at: https://github.com/cisco-open/DeepVision/tree/main/recall

Computational models for semantic textual similarity

164 p.The overarching goal of this thesis is to advance on computational models of meaning and their evaluation. To achieve this goal we define two tasks and develop state-of-the-art systems that tackle both task: Semantic Textual Similarity (STS) and Typed Similarity.STS aims to measure the degree of semantic equivalence between two sentences by assigning graded similarity values that capture the intermediate shades of similarity. We have collected pairs of sentences to construct datasets for STS, a total of 15,436 pairs of sentences, being by far the largest collection of data for STS.We have designed, constructed and evaluated a new approach to combine knowledge-based and corpus-based methods using a cube. This new system for STS is on par with state-of-the-art approaches that make use of Machine Learning (ML) without using any of it, but ML can be used on this system, improving the results.Typed Similarity tries to identify the type of relation that holds between a pair of similar items in a digital library. Providing a reason why items are similar has applications in recommendation, personalization, and search. A range of types of similarity in this collection were identified and a set of 1,500 pairs of items from the collection were annotated using crowdsourcing.Finally, we present systems capable of resolving the Typed Similarity task. The best system resulted in a real-world application to recommend similar items to users in an online digital library