Topology of biological networks and reliability of information processing

Biological systems rely on robust internal information processing: Survival depends on highly reproducible dynamics of regulatory processes. Biological information processing elements, however, are intrinsically noisy (genetic switches, neurons, etc.). Such noise poses severe stability problems to system behavior as it tends to desynchronize system dynamics (e.g. via fluctuating response or transmission time of the elements). Synchronicity in parallel information processing is not readily sustained in the absence of a central clock. Here we analyze the influence of topology on synchronicity in networks of autonomous noisy elements. In numerical and analytical studies we find a clear distinction between non-reliable and reliable dynamical attractors, depending on the topology of the circuit. In the reliable cases, synchronicity is sustained, while in the unreliable scenario, fluctuating responses of single elements can gradually desynchronize the system, leading to non-reproducible behavior. We find that the fraction of reliable dynamical attractors strongly correlates with the underlying circuitry. Our model suggests that the observed motif structure of biological signaling networks is shaped by the biological requirement for reproducibility of attractors.Comment: 7 pages, 7 figure

Interaction history based answer formulation for question answering

With the rapid growth in information access methodologies, question answering has drawn considerable attention among others. Though question answering has emerged as an interesting new research domain, still it is vastly concentrated on question processing and answer extraction approaches. Latter steps like answer ranking, formulation and presentations are not treated in depth. Weakness we found in this arena is that answers that a particular user has acquired are not considered, when processing new questions. As a result, current systems are not capable of linking two questions such as “When is the Apple founded?” with a previously processed question “When is the Microsoft founded?” generating an answer in the form of “Apple is founded one year later Microsoft founded, in 1976”. In this paper we present an approach towards question answering to devise an answer based on the questions already processed by the system for a particular user which is termed as interaction history for the user. Our approach is a combination of question processing, relation extraction and knowledge representation with inference models. During the process we primarily focus on acquiring knowledge and building up a scalable user model to formulate future answers based on current answers that same user has processed. According to evaluation we carried out based on the TREC resources shows that proposed technology is promising and effective in question answering

Cross-lingual question answering

Question Answering has become an intensively researched area in the last decade, being seen as the next step beyond Information Retrieval in the attempt to provide more concise and better access to large volumes of available information. Question Answering builds on Information Retrieval technology for a first touch of possible relevant data and uses further natural language processing techniques to search for candidate answers and to look for clues that accept or invalidate the candidates as right answers to the question. Though most of the research has been carried out in monolingual settings, where the question and the answer-bearing documents share the same natural language, current approaches concentrate on cross-language scenarios, where the question and the documents are in different languages. Known in this context and common with the Information Retrieval research are three methods of crossing the language barrier: by translating the question, by translating the documents or by aligning both the question and the documents to a common inter-lingual representation. We present a cross-lingual English to German Question Answering system, for both factoid and definition questions, using a German monolingual system and translating the questions from English to German. Two different techniques of translation are evaluated: • direct translation of the English input question into German and • transfer-based translation, by using an intermediate representation that captures the “meaning” of the original question and is translated into the target language. For both translation techniques two types of translation tools are used: bilingual dictionaries and machine translation. The intermediate representation captures the semantic meaning of the question in terms of Question Type (QType), Expected Answer Type (EAType) and Focus, information that steers the workflow of the question answering process. The German monolingual Question Answering system can answer both factoid and definition questions and is based on several premises: • facts and definitions are usually expressed locally at the level of a sentence and its surroundings; • proximity of concepts within a sentence can be related to their semantic dependency; • for factoid questions, redundancy of candidate answers is a good indicator of their suitability; • definitions of concepts are expressed using fixed linguistic structures such as appositions, modifiers, and abbreviation extensions. Extensive evaluations of the monolingual system have shown that the above mentioned hypothesis holds true in most of the cases when dealing with a fairly large collection of documents, like the one used in the CLEF evaluation forum.Innerhalb der letzten zehn Jahre hat sich Question Answering zu einem intensiv erforschten Themengebiet gewandelt, es stellt den nächsten Schritt des Information Retrieval dar, mit dem Bestreben einen präziseren Zugang zu großen Datenbeständen von verfügbaren Informationen bereitzustellen. Das Question Answering setzt auf die Information Retrieval-Technologie, um mögliche relevante Daten zu suchen, kombiniert mit weiteren Techniken zur Verarbeitung von natürlicher Sprache, um mögliche Antwortkandidaten zu identifizieren und diese anhand von Hinweisen oder Anhaltspunkten entsprechend der Frage als richtige Antwort zu akzeptieren oder als unpassend zu erklären. Während ein Großteil der Forschung den einsprachigen Kontext voraussetzt, wobei Frage- und Antwortdokumente ein und dieselbe Sprache teilen, konzentrieren sich aktuellere Ansätze auf sprachübergreifende Szenarien, in denen die Frage- und Antwortdokumente in unterschiedlichen Sprachen vorliegen. Im Kontext des Information Retrieval existieren drei bekannte Ansätze, die versuchen auf unterschiedliche Art und Weise die Sprachbarriere zu überwinden: durch die Übersetzung der Frage, durch die Übersetzung der Dokumente oder durch eine Angleichung von sowohl der Frage als auch der Dokumente zu einer gemeinsamen interlingualen Darstellung. Wir präsentieren ein sprachübergreifendes Question Answering System vom Englischen ins Deutsche, das sowohl für Faktoid- als auch für Definitionsfragen funktioniert. Dazu verwenden wir ein einsprachiges deutsches System und übersetzen die Fragen vom Englischen ins Deutsche. Zwei unterschiedliche Techniken der Übersetzung werden untersucht: • die direkte Übersetzung der englischen Fragestellung ins Deutsche und • die Abbildungs-basierte Übersetzung, die eine Zwischendarstellung verwendet, um die „Semantik“ der ursprünglichen Frage zu erfassen und in die Zielsprache zu übersetzen. Für beide aufgelisteten Übersetzungstechniken werden zwei Übersetzungsquellen verwendet: zweisprachige Wörterbücher und maschinelle Übersetzung. Die Zwischendarstellung erfasst die Semantik der Frage in Bezug auf die Art der Frage (QType), den erwarteten Antworttyp (EAType) und Fokus, sowie die Informationen, die den Ablauf des Frage-Antwort-Prozesses steuern. Das deutschsprachige Question Answering System kann sowohl Faktoid- als auch Definitionsfragen beantworten und basiert auf mehreren Prämissen: • Fakten und Definitionen werden in der Regel lokal auf Satzebene ausgedrückt; • Die Nähe von Konzepten innerhalb eines Satzes kann auf eine semantische Verbindung hinweisen; • Bei Faktoidfragen ist die Redundanz der Antwortkandidaten ein guter Indikator für deren Eignung; • Definitionen von Begriffen werden mit festen sprachlichen Strukturen ausgedrückt, wie Appositionen, Modifikatoren, Abkürzungen und Erweiterungen. Umfangreiche Auswertungen des einsprachigen Systems haben gezeigt, dass die oben genannten Hypothesen in den meisten Fällen wahr sind, wenn es um eine ziemlich große Sammlung von Dokumenten geht, wie bei der im CLEF Evaluationsforum verwendeten Version

Tracking Context in Conversational Search: From Utterances to Neural Embeddings

The use of conversational assistants is becoming increasingly more popular among the general public, pushing the research towards more advanced and sophisticated techniques. Hence, there are currently a number of research opportunities to extend the comprehension and applicability of these tasks in everyday systems. These conversational assistants are capable of performing various tasks, such as chitchatting, internal device functions (e.g., setting up an alarm), and searching for information. In the last few years, the interest in conversational search is increasing, not only because of the generalization of conversational assistants but also because conversational search is a step forward in allowing a more natural interaction with the system. To build a system such as this, many components need to work together, since in a conversation, the importance of context is paramount to retrieve the best answers to the user’s questions. In this thesis, the focus was on developing a conversational search system that aims to help people search for information in a natural way. In particular, this system must be able to understand the context where the question is posed, tracking the current state of the conversation and detecting mentions to previous questions and answers. We achieve this by using a context-tracking component based on neural query-rewriting models. Another crucial aspect of the system is to provide the most relevant answers given the question and the conversational history. To achieve this objective, we used state-of-the-art retrieval and re-ranking methods and expanded their architecture to use the conversational context. The results obtained with the system developed achieved state-of-the-art when compared to the baselines present in TREC Conversational Assistance Track (CAsT) 2019.O uso de assistentes conversacionais está a tornar-se cada vez mais popular entre o público em geral, levando à investigação de técnicas mais avançadas e sofisticadas. Consequentemente, existem atualmente várias oportunidades de investigação para estender a compreensão e aplicabilidade destas tarefas em sistemas do quotidiano. Estes assistentes são capazes de efetuar várias tarefas como, por exemplo: ter uma conversa informal, efetuar funções internas ao dispositivo (e.g. colocar um alarme), e pesquisar por informação. Nos últimos anos, o interesse em pesquisa conversacional tem estado a aumentar, não só pela generalização dos assistentes conversacionais, mas também devido a ser um passo em frente para permitir uma interação mais natural com o sistema. Para construir um sistema deste tipo, vários componentes têm de trabalhar em conjunto, uma vez que numa conversa o contexto é da maior importância para recuperar as melhores respostas para as perguntas do utilizador. Nesta tese, o foco foi desenvolver um sistema de pesquisa conversacional para ajudar as pessoas a pesquisar por informação de uma forma natural. Em particular, este sistema tem de ser capaz de compreender o contexto onde a questão é colocada, fazendo tracking do estado atual da conversa e detetando menções a perguntas e respostas anteriores. Com esse objetivo, desenvolvemos um componente de tracking de contexto baseado em modelos neuronais de reescrita de perguntas. Outro aspeto crucial deste sistema é fornecer as respostas mais relevantes dada uma pergunta e o histórico da conversa. Para alcançar este objetivo, utilizámos modelos do estado-da-arte em recuperação de informação e re-ranking e expandimos estas arquiteturas de modo a utilizarem o contexto da conversa. Os resultados obtidos com o sistema desenvolvido atingiram resultados do estado.da-arte quando comparados às baselines submetidas no TREC Conversational Assistance Track (CAsT) 2019

Relation based models for passage retrieval in open domain question answering

Master'sMASTER OF SCIENC

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report

Judgments of learning and improvement

Can learners accurately judge the rate of their learning? Rates of learning may be informative when study time is allocated across materials, and students' judgments of their learning rate have been proposed as a possible metacognitive tool. Participants estimated how much they improved between presentations in multitrial learning situations in which n-gram paragraphs (in Experiments 1 and 2) or word pairs (Experiments 3 and 4) were learned . In the first experiment, participants rated improvement on a percentage scale, whereas on the second and third, judgments were given on a 0–6 scale. Experiment 4 used both a percentage scale and an absolute number scale. The main result was that judgments of improvement were poorly correlated with actual improvement and, in one case, were negatively correlated. Although judgments of improvement were correlated with changes in judgments of learning, they were not reliable indicators of actual improvement. Implications are discussed for theoretical work on metacognition