Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Metaphor Aptness And Conventionality: A Processing Fluency Account

Conventionality and aptness are two dimensions of metaphorical sentences thought to play an important role in determining how quick and easy it is to process a metaphor. Conventionality reflects the familiarity of a metaphor whereas aptness reflects the degree to which a metaphor vehicle captures important features of a metaphor topic. In recent years it has become clear that operationalizing these two constructs is not as simple as asking naïve raters for subjective judgments. It has been found that ratings of aptness and conventionality are highly correlated, which has led some researchers to pursue alternative methods for measuring the constructs. Here, in four experiments, we explore the underlying reasons for the high correlation in ratings of aptness and conventionality, and question the construct validity of various methods for measuring the two dimensions. We find that manipulating the processing fluency of a metaphorical sentence by means of familiarization to similar senses of the metaphor (“in vivo conventionalization”) influences ratings of the sentence\u27s aptness. This misattribution may help explain why subjective ratings of aptness and conventionality are highly correlated. In addition, we find other reasons to question the construct validity of conventionality and aptness measures: for instance, we find that conventionality is context dependent and thus not attributable to a metaphor vehicle alone, and we find that ratings of aptness take more into account than they should

Doctor of Philosophy in Computer Science

dissertationOver the last decade, social media has emerged as a revolutionary platform for informal communication and social interactions among people. Publicly expressing thoughts, opinions, and feelings is one of the key characteristics of social media. In this dissertation, I present research on automatically acquiring knowledge from social media that can be used to recognize people's affective state (i.e., what someone feels at a given time) in text. This research addresses two types of affective knowledge: 1) hashtag indicators of emotion consisting of emotion hashtags and emotion hashtag patterns, and 2) affective understanding of similes (a form of figurative comparison). My research introduces a bootstrapped learning algorithm for learning hashtag in- dicators of emotions from tweets with respect to five emotion categories: Affection, Anger/Rage, Fear/Anxiety, Joy, and Sadness/Disappointment. With a few seed emotion hashtags per emotion category, the bootstrapping algorithm iteratively learns new hashtags and more generalized hashtag patterns by analyzing emotion in tweets that contain these indicators. Emotion phrases are also harvested from the learned indicators to train additional classifiers that use the surrounding word context of the phrases as features. This is the first work to learn hashtag indicators of emotions. My research also presents a supervised classification method for classifying affective polarity of similes in Twitter. Using lexical, semantic, and sentiment properties of different simile components as features, supervised classifiers are trained to classify a simile into a positive or negative affective polarity class. The property of comparison is also fundamental to the affective understanding of similes. My research introduces a novel framework for inferring implicit properties that 1) uses syntactic constructions, statistical association, dictionary definitions and word embedding vector similarity to generate and rank candidate properties, 2) re-ranks the top properties using influence from multiple simile components, and 3) aggregates the ranks of each property from different methods to create a final ranked list of properties. The inferred properties are used to derive additional features for the supervised classifiers to further improve affective polarity recognition. Experimental results show substantial improvements in affective understanding of similes over the use of existing sentiment resources

Curvature-based sparse rule base generation for fuzzy rule interpolation

Fuzzy logic has been successfully widely utilised in many real-world applications. The most common application of fuzzy logic is the rule-based fuzzy inference system, which is composed of mainly two parts including an inference engine and a fuzzy rule base. Conventional fuzzy inference systems always require a rule base that fully covers the entire problem domain (i.e., a dense rule base). Fuzzy rule interpolation (FRI) makes inference possible with sparse rule bases which may not cover some parts of the problem domain (i.e., a sparse rule base). In addition to extending the applicability of fuzzy inference systems, fuzzy interpolation can also be used to reduce system complexity for over-complex fuzzy inference systems. There are typically two methods to generate fuzzy rule bases, i.e., the knowledge driven and data-driven approaches. Almost all of these approaches only target dense rule bases for conventional fuzzy inference systems. The knowledge-driven methods may be negatively affected by the limited availability of expert knowledge and expert knowledge may be subjective, whilst redundancy often exists in fuzzy rule-based models that are acquired from numerical data. Note that various rule base reduction approaches have been proposed, but they are all based on certain similarity measures and are likely to cause performance deterioration along with the size reduction. This project, for the first time, innovatively applies curvature values to distinguish important features and instances in a dataset, to support the construction of a neat and concise sparse rule base for fuzzy rule interpolation. In addition to working in a three-dimensional problem space, the work also extends the natural three-dimensional curvature calculation to problems with high dimensions, which greatly broadens the applicability of the proposed approach. As a result, the proposed approach alleviates the ‘curse of dimensionality’ and helps to reduce the computational cost for fuzzy inference systems. The proposed approach has been validated and evaluated by three real-world applications. The experimental results demonstrate that the proposed approach is able to generate sparse rule bases with less rules but resulting in better performance, which confirms the power of the proposed system. In addition to fuzzy rule interpolation, the proposed curvature-based approach can also be readily used as a general feature selection tool to work with other machine learning approaches, such as classifiers

Figuration & Frequency: A Usage-Based Approach to Metaphor

Two of the major claims of the cognitivist approach to metaphor, the paradigm which has emerged as dominant over the last three decades, are 1) that metaphor is a conceptual, rather than strictly linguistic, phenomenon, and 2) that metaphor exemplifies processes which are at work in cognition more generally. This view of metaphor is here placed within the context of the functionalist approach to language, which asserts that linguistic structure is emergent in nature, the use of language directly influencing the storage and representation thereof. The dissertation argues that metaphors, as conventionalized cognitive structures, are themselves highly influenced by frequency effects, and that metaphorical cross-domain mappings exist in the mind as conceptual schemata. Two corpus-based methods for assessing the frequency of overall metaphorical mappings are presented, both based on the use of key terms, attained using a survey method, for metaphorical source domains. These findings inform the hypotheses of a series of three experiments which test three key predictions of the view that metaphors are affected by frequency: that frequent metaphors should be more productive, accessible, and acceptable than infrequent ones. Both the corpus and experimental approaches, as well as data from previous research on metaphor at varying levels of conventionalization, support the view that metaphors are a usage-based phenomenon. The properties of various types of metaphorical utterances (e.g., idioms and novel metaphors) are best accounted for as arising from the interaction of the conceptual schemata that license cross-domain mappings, and syntactic schemata that link meanings to syntactic templates

MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base

The ability to understand and generate similes is an imperative step to realize human-level AI. However, there is still a considerable gap between machine intelligence and human cognition in similes, since deep models based on statistical distribution tend to favour high-frequency similes. Hence, a large-scale symbolic knowledge base of similes is required, as it contributes to the modeling of diverse yet unpopular similes while facilitating additional evaluation and reasoning. To bridge the gap, we propose a novel framework for large-scale simile knowledge base construction, as well as two probabilistic metrics which enable an improved understanding of simile phenomena in natural language. Overall, we construct MAPS-KB, a million-scale probabilistic simile knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB corpora. We conduct sufficient experiments to justify the effectiveness and necessity of the methods of our framework. We also apply MAPS-KB on three downstream tasks to achieve state-of-the-art performance, further demonstrating the value of MAPS-KB.Comment: Accepted to AAAI 202

Understanding analogical reasoning : viewpoints from psychology and related disciplines

Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning

“LUMINESCENT AS AN ANGLERFISH”: CREATIVE WRITING AS A STRATEGY FOR BUILDING FIGURATIVE LANGUAGE SKILLS IN SCHOOL-AGED CHILDREN

This pretest/ posttest nonequivalent groups study explored the relationship between classroom-based creative writing instruction and the figurative language abilities of fourth grade students. Figurative language is widespread within the oral and written discourse of K-12 classrooms and is an essential component of higher-level language and literacy development. Despite the prevalence of non-literal language in educational settings and its relevance to children’s academic and social success, research concerning best practices for teaching non-literal language remains scarce. A few studies have suggested that creative writing may be an effective vehicle for fostering figurative language in children. Poetry writing seems especially promising, since poetry is rich in figurative forms and tends to be motivational for young writers. In this study, I compared pretest and posttest scores on a brief measure of figurative language which I administered to two groups of fourth grade students. The treatment group (n = 30) received six weeks of poetry writing instruction between pretest and posttest, while the comparison group (n = 37) did not. Results of a within subjects analysis using paired samples t tests revealed that only the treatment group demonstrated significant gains on the posttest. Results of between subjects analysis showed that the change in the treatment group’s scores between differed significantly from the comparison group’s change in scores. The effect size was large for both the within subjects and the between subjects analyses. Although generalizability is limited due to the nonrandomized design, the results suggest that creative writing deserves more attention as a means of teaching figurative language to school-aged children