338 research outputs found
An integrated theory of language production and comprehension
Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal
Towards a complete multiple-mechanism account of predictive language processing [Commentary on Pickering & Garrod]
Although we agree with Pickering & Garrod (P&G) that prediction-by-simulation and prediction-by-association are important mechanisms of anticipatory language processing, this commentary suggests that they: (1) overlook other potential mechanisms that might underlie prediction in language processing, (2) overestimate the importance of prediction-by-association in early childhood, and (3) underestimate the complexity and significance of several factors that might mediate prediction during language processing
Survey on reinforcement learning for language processing
In recent years some researchers have explored the use of reinforcement
learning (RL) algorithms as key components in the solution of various natural
language processing tasks. For instance, some of these algorithms leveraging
deep neural learning have found their way into conversational systems. This
paper reviews the state of the art of RL methods for their possible use for
different problems of natural language processing, focusing primarily on
conversational systems, mainly due to their growing relevance. We provide
detailed descriptions of the problems as well as discussions of why RL is
well-suited to solve them. Also, we analyze the advantages and limitations of
these methods. Finally, we elaborate on promising research directions in
natural language processing that might benefit from reinforcement learning
Recommended from our members
A Quantum-like Multimodal Network Framework for Modeling Interaction Dynamics in Multiparty Conversational Sentiment Analysis
Sentiment analysis in conversations is an emerging yet challenging artificial intelligence (AI) task. It aims to discover the affective states and emotional changes of speakers involved in a conversation on the basis of their opinions, which are carried by different modalities of information (e.g., a video associated with a transcript). There exists a wealth of intra- and inter-utterance interaction information that affects the emotions of speakers in a complex and dynamic way. How to accurately and comprehensively model complicated interactions is the key problem of the field. To fill this gap, in this paper, we propose a novel and comprehensive framework for multimodal sentiment analysis in conversations, called a quantum-like multimodal network (QMN), which leverages the mathematical formalism of quantum theory (QT) and a long short-term memory (LSTM) network. Specifically, the QMN framework consists of a multimodal decision fusion approach inspired by quantum interference theory to capture the interactions within each utterance (i.e., the correlations between different modalities) and a strong-weak influence model inspired by quantum measurement theory to model the interactions between adjacent utterances (i.e., how one speaker influences another). Extensive experiments are conducted on two widely used conversational sentiment datasets: the MELD and IEMOCAP datasets. The experimental results show that our approach significantly outperforms a wide range of baselines and state-of-the-art models
Statistical natural language processing methods for intelligent process automation
Nowadays, digitization is transforming the way businesses work. Recently, Artificial Intelligence (AI) techniques became an essential part of the automation of business processes: In addition to cost advantages, these techniques offer fast processing times and higher customer satisfaction rates, thus ultimately increasing sales. One of the intelligent approaches for accelerating digital transformation in companies is the Robotic Process Automation (RPA).
An RPA-system is a software tool that robotizes routine and time-consuming responsibilities such as email assessment, various calculations, or creation of documents and reports (Mohanty and Vyas, 2018). Its main objective is to organize a smart workflow and therethrough to assist employees by offering them more scope for cognitively demanding and engaging work.
Intelligent Process Automation (IPA) offers all these advantages as well; however, it goes beyond the RPA by adding AI components such as Machine- and Deep Learning techniques to conventional automation solutions. Previously, IPA approaches were primarily employed within the computer vision domain. However, in recent times, Natural Language Processing (NLP) became one of the potential applications for IPA as well due to its ability to understand and interpret human language. Usually, NLP methods are used to analyze large amounts of unstructured textual data and to respond to various inquiries. However, one of the central applications of NLP within the IPA domain â are conversational interfaces (e.g., chatbots, virtual agents) that are used to enable human-to-machine communication. Nowadays, conversational agents gain enormous demand due to their ability to support a large number of users simultaneously while communicating in a natural language. The implementation of a conversational agent comprises multiple stages and involves diverse types of NLP sub-tasks, starting with natural language understanding (e.g., intent recognition, named entity extraction) and going towards dialogue management (i.e., determining the next possible bots action) and response generation. Typical dialogue system for IPA purposes undertakes straightforward customer support requests (e.g., FAQs), allowing human workers to focus on more complicated inquiries.
In this thesis, we are addressing two potential Intelligent Process Automation (IPA) applications and employing statistical Natural Language Processing (NLP) methods for their implementation.
The first block of this thesis (Chapter 2 â Chapter 4) deals with the development of a conversational agent for IPA purposes within the e-learning domain. As already mentioned, chatbots are one of the central applications for the IPA domain since they can effectively perform time-consuming tasks while communicating in a natural language. Within this thesis, we realized the IPA conversational bot that takes care of routine and time-consuming tasks regularly performed by human tutors of an online mathematical course. This bot is deployed in a real-world setting within the OMB+ mathematical platform. Conducting experiments for this part, we observed two possibilities to build the conversational agent in industrial settings â first, with purely rule-based methods, considering the missing training data and individual aspects of the target domain (i.e., e-learning). Second, we re-implemented two of the main system components (i.e., Natural Language Understanding (NLU) and Dialogue Manager (DM) units) using the current state-of-the-art deep-learning architecture (i.e., Bidirectional Encoder Representations from Transformers (BERT)) and investigated their performance and potential use as a part of a hybrid model (i.e., containing both rule-based and machine learning methods).
The second part of the thesis (Chapter 5 â Chapter 6) considers an IPA subproblem within the predictive analytics domain and addresses the task of scientific trend forecasting. Predictive analytics forecasts future outcomes based on historical and current data. Therefore, using the benefits of advanced analytics models, an organization can, for instance, reliably determine trends and emerging topics and then manipulate it while making significant business decisions (i.e., investments). In this work, we dealt with the trend detection task â specifically, we addressed the lack of publicly available benchmarks for evaluating trend detection algorithms. We assembled the benchmark for the detection of both scientific trends and downtrends (i.e., topics that become less frequent overtime). To the best of our knowledge, the task of downtrend detection has not been addressed before. The resulting benchmark is based on a collection of more than one million documents, which is among the largest that has been used for trend detection before, and therefore, offers a realistic setting for the development of trend detection algorithms.Robotergesteuerte Prozessautomatisierung (RPA) ist eine Art von Software-Bots, die manuelle menschliche TĂ€tigkeiten wie die Eingabe von Daten in das System, die Anmeldung in Benutzerkonten oder die AusfĂŒhrung einfacher, aber sich wiederholender ArbeitsablĂ€ufe nachahmt (Mohanty and Vyas, 2018). Einer der Hauptvorteile und gleichzeitig Nachteil der RPA-bots ist jedoch deren FĂ€higkeit, die gestellte Aufgabe punktgenau zu erfĂŒllen. Einerseits ist ein solches System in der Lage, die Aufgabe akkurat, sorgfĂ€ltig und schnell auszufĂŒhren. Andererseits ist es sehr anfĂ€llig fĂŒr VerĂ€nderungen in definierten Szenarien. Da der RPA-Bot fĂŒr eine bestimmte Aufgabe konzipiert ist, ist es oft nicht möglich, ihn an andere DomĂ€nen oder sogar fĂŒr einfache Ănderungen in einem Arbeitsablauf anzupassen (Mohanty and Vyas, 2018). Diese UnfĂ€higkeit, sich an verĂ€nderte Bedingungen anzupassen, fĂŒhrte zu einem weiteren Verbesserungsbereich fĂŒr RPAbots â den Intelligenten Prozessautomatisierungssystemen (IPA).
IPA-Bots kombinieren RPA mit KĂŒnstlicher Intelligenz (AI) und können komplexe und kognitiv anspruchsvollere Aufgaben erfĂŒllen, die u.A. Schlussfolgerungen und natĂŒrliches SprachverstĂ€ndnis erfordern. Diese Systeme ĂŒbernehmen zeitaufwĂ€ndige und routinemĂ€Ăige Aufgaben, ermöglichen somit einen intelligenten Arbeitsablauf und befreien FachkrĂ€fte fĂŒr die DurchfĂŒhrung komplizierterer Aufgaben. Bisher wurden die IPA-Techniken hauptsĂ€chlich im Bereich der Bildverarbeitung eingesetzt. In der letzten Zeit wurde die natĂŒrliche
Sprachverarbeitung (NLP) jedoch auch zu einem der potenziellen Anwendungen fĂŒr IPA, und zwar aufgrund von der FĂ€higkeit, die menschliche Sprache zu interpretieren. NLP-Methoden werden eingesetzt, um groĂe Mengen an Textdaten zu analysieren und auf verschiedene
Anfragen zu reagieren. Auch wenn die verfĂŒgbaren Daten unstrukturiert sind oder kein vordefiniertes Format haben (z.B. E-Mails), oder wenn die in einem variablen Format vorliegen (z.B. Rechnungen, juristische Dokumente), dann werden ebenfalls die NLP Techniken angewendet, um die relevanten Informationen zu extrahieren, die dann zur Lösung verschiedener Probleme verwendet werden können.
NLP im Rahmen von IPA beschrĂ€nkt sich jedoch nicht auf die Extraktion relevanter Daten aus Textdokumenten. Eine der zentralen Anwendungen von IPA sind Konversationsagenten, die zur Interaktion zwischen Mensch und Maschine eingesetzt werden. Konversationsagenten erfahren enorme Nachfrage, da sie in der Lage sind, eine groĂe Anzahl von Benutzern gleichzeitig zu unterstĂŒtzen, und dabei in einer natĂŒrlichen Sprache kommunizieren. Die Implementierung
eines Chatsystems umfasst verschiedene Arten von NLP-Teilaufgaben, beginnend mit dem VerstĂ€ndnis der natĂŒrlichen Sprache (z.B. Absichtserkennung, Extraktion von EntitĂ€ten) ĂŒber das Dialogmanagement (z.B. Festlegung der nĂ€chstmöglichen Bot-Aktion) bis hin zur Response-Generierung. Ein typisches Dialogsystem fĂŒr IPA-Zwecke ĂŒbernimmt in der Regel unkomplizierte Kundendienstanfragen (z.B. Beantwortung von FAQs), so dass sich die Mitarbeiter auf komplexere Anfragen konzentrieren können.
Diese Dissertation umfasst zwei Bereiche, die durch das breitere Thema vereint sind, nĂ€mlich die Intelligente Prozessautomatisierung (IPA) unter Verwendung statistischer Methoden der natĂŒrlichen Sprachverarbeitung (NLP).
Der erste Block dieser Arbeit (Kapitel 2 â Kapitel 4) befasst sich mit der Impementierung eines Konversationsagenten fĂŒr IPA-Zwecke innerhalb der E-Learning-DomĂ€ne. Wie bereits erwĂ€hnt, sind Chatbots eine der zentralen Anwendungen fĂŒr die IPA-DomĂ€ne, da sie zeitaufwĂ€ndige Aufgaben in einer natĂŒrlichen Sprache effektiv ausfĂŒhren können. Der IPA-Kommunikationsbot, der in dieser Arbeit realisiert wurde, kĂŒmmert sich ebenfalls um routinemĂ€Ăige und zeitaufwĂ€ndige Aufgaben, die sonst von Tutoren in einem Online-Mathematikkurs in deutscher Sprache durchgefĂŒhrt werden. Dieser Bot ist in der tĂ€glichen Anwendung innerhalb der mathematischen Plattform OMB+ eingesetzt. Bei der DurchfĂŒhrung von Experimenten beobachteten wir zwei Möglichkeiten, den Konversationsagenten im industriellen Umfeld zu entwickeln â zunĂ€chst mit rein regelbasierten Methoden, unter Bedingungen der fehlenden Trainingsdaten und besonderer Aspekte der ZieldomĂ€ne (d.h. E-Learning). Zweitens haben wir zwei der Hauptsystemkomponenten (SprachverstĂ€ndnismodul, Dialog-Manager) mit dem derzeit fortschrittlichsten Deep Learning Algorithmus reimplementiert und die Performanz dieser Komponenten untersucht.
Der zweite Teil der Doktorarbeit (Kapitel 5 â Kapitel 6) betrachtet ein IPA-Problem innerhalb des Vorhersageanalytik-Bereichs. Vorhersageanalytik zielt darauf ab, Prognosen ĂŒber zukĂŒnftige Ergebnisse auf der Grundlage von historischen und aktuellen Daten zu erstellen. Daher kann ein Unternehmen mit Hilfe der Vorhersagesysteme z.B. die Trends oder neu entstehende Themen zuverlĂ€ssig bestimmen und diese Informationen dann bei wichtigen GeschĂ€ftsentscheidungen (z.B. Investitionen) einsetzen. In diesem Teil der Arbeit beschĂ€ftigen wir uns mit dem Teilproblem der Trendprognose â insbesondere mit dem Fehlen öffentlich zugĂ€nglicher Benchmarks fĂŒr die Evaluierung von Trenderkennungsalgorithmen. Wir haben den Benchmark zusammengestellt und veröffentlicht, um sowohl Trends als auch AbwĂ€rtstrends zu erkennen. Nach unserem besten Wissen ist die Aufgabe der AbwĂ€rtstrenderkennung bisher nicht adressiert worden. Der resultierende Benchmark basiert auf einer Sammlung von mehr als einer Million Dokumente, der zu den gröĂten gehört, die bisher fĂŒr die Trenderkennung verwendet wurden, und somit einen realistischen Rahmen fĂŒr die Entwicklung von Trenddetektionsalgorithmen bietet
Recent Trends in Deep Learning Based Personality Detection
Recently, the automatic prediction of personality traits has received a lot
of attention. Specifically, personality trait prediction from multimodal data
has emerged as a hot topic within the field of affective computing. In this
paper, we review significant machine learning models which have been employed
for personality detection, with an emphasis on deep learning-based methods.
This review paper provides an overview of the most popular approaches to
automated personality detection, various computational datasets, its industrial
applications, and state-of-the-art machine learning models for personality
detection with specific focus on multimodal approaches. Personality detection
is a very broad and diverse topic: this survey only focuses on computational
approaches and leaves out psychological studies on personality detection
User Satisfaction Reward Estimation Across Domains: Domain-independent Dialogue Policy Learning
Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work that is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we propose to use a reward signal based on user satisfaction. We propose a novel estimator and show that it outperforms all previous estimators while learning temporal dependencies implicitly. We show in simulated experiments that a live user satisfaction estimation model may be applied resulting in higher estimated satisfaction whilst achieving similar success rates. Moreover, we show that a satisfaction estimation model trained on one domain may be applied in many other domains that cover a similar task. We verify our findings by employing the model to one of the domains for learning a policy from real users and compare its performance to policies using user satisfaction and task success acquired directly from the users as reward
- âŠ