Gesture in Automatic Discourse Processing

Computers cannot fully understand spoken language without access to the wide range of modalities that accompany speech. This thesis addresses the particularly expressive modality of hand gesture, and focuses on building structured statistical models at the intersection of speech, vision, and meaning.My approach is distinguished in two key respects. First, gestural patterns are leveraged to discover parallel structures in the meaning of the associated speech. This differs from prior work that attempted to interpret individual gestures directly, an approach that was prone to a lack of generality across speakers. Second, I present novel, structured statistical models for multimodal language processing, which enable learning about gesture in its linguistic context, rather than in the abstract.These ideas find successful application in a variety of language processing tasks: resolving ambiguous noun phrases, segmenting speech into topics, and producing keyframe summaries of spoken language. In all three cases, the addition of gestural features -- extracted automatically from video -- yields significantly improved performance over a state-of-the-art text-only alternative. This marks the first demonstration that hand gesture improves automatic discourse processing

Improved Coreference Resolution Using Cognitive Insights

Coreference resolution is the task of extracting referential expressions, or mentions, in text and clustering these by the entity or concept they refer to. The sustained research interest in the task reflects the richness of reference expression usage in natural language and the difficulty in encoding insights from linguistic and cognitive theories effectively. In this thesis, we design and implement LIMERIC, a state-of-the-art coreference resolution engine. LIMERIC naturally incorporates both non-local decoding and entity-level modelling to achieve the highly competitive benchmark performance of 64.22% and 59.99% on the CoNLL-2012 benchmark with a simple model and a baseline feature set. As well as strong performance, a key contribution of this work is a reconceptualisation of the coreference task. We draw an analogy between shift-reduce parsing and coreference resolution to develop an algorithm which naturally mimics cognitive models of human discourse processing. In our feature development work, we leverage insights from cognitive theories to improve our modelling. Each contribution achieves statistically significant improvements and sum to gains of 1.65% and 1.66% on the CoNLL-2012 benchmark, yielding performance values of 65.76% and 61.27%. For each novel feature we propose, we contribute an accompanying analysis so as to better understand how cognitive theories apply to real language data. LIMERIC is at once a platform for exploring cognitive insights into coreference and a viable alternative to current systems. We are excited by the promise of incorporating our and further cognitive insights into more complex frameworks since this has the potential to both improve the performance of computational models, as well as our understanding of the mechanisms underpinning human reference resolution

References to graphical objects in interactive multimodel queries

This thesis describes a computational model for interpreting natural language expressions in an interactive multimodal query system integrating both natural language text and graphic displays. The primary concern of the model is to interpret expressions that might involve graphical attributes, and expressions whose referents could be objects on the screen.Graphical objects on the screen are used to visualise entities in the application domain and their attributes (in short, domain entities and domain attributes). This is why graphical objects are treated as descriptions of those domain entities/attributes in the literature. However, graphical objects and their attributes are visible during the interaction, and are thus known by the participants of the interaction. Therefore, they themselves should be part of the mutual knowledge of the interaction.This poses some interesting problems in language processing. As part of the mutual knowledge, graphical attributes could be used in expressions, and graphical objects could be referred to by expressions. In consequence, there could be ambiguities about whether an attribute in an expression belongs to a graphical object or to a domain entity. There could also be ambiguities about whether the referent of an expression is a graphical object or a domain entity.The main contributions of this thesis consist of analysing the above ambiguities, de¬ signing, implementing and testing a computational model and a demonstration system for resolving these ambiguities. Firstly, a structure and corresponding terminology are set up, so these ambiguities can be clarified as ambiguities derived from referring to different databases, the screen or the application domain (source ambiguities). Secondly, a meaning representation language is designed which explicitly represents the information about which database an attribute/entity comes from. Several linguistic regularities inside and among referring expressions are described so that they can be used as heuristics in the ambiguity resolution. Thirdly, a computational model based on constraint satisfaction is constructed to resolve simultaneously some reference ambiguities and source ambiguities. Then, a demonstration system integrating natural language text and graphics is implemented, whose core is the computational model.This thesis ends with an evaluation of the computational model. It provides some concrete evidence about the advantages and disadvantages of the above approach

Automatic Question Generation Using Semantic Role Labeling for Morphologically Rich Languages

In this paper, a novel approach to automatic question generation (AQG) using semantic role labeling (SRL) for morphologically rich languages is presented. A model for AQG is developed for our native speaking language, Croatian. Croatian language is a highly inflected language that belongs to Balto-Slavic family of languages. Globally this article can be divided into two stages. In the first stage we present a novel approach to SRL of texts written in Croatian language that uses Conditional Random Fields (CRF). SRL traditionally consists of predicate disambiguation, argument identification and argument classification. After these steps most approaches use beam search to find optimal sequence of arguments based on given predicate. We propose the architecture for predicate identification and argument classification in which finding the best sequence of arguments is handled by Viterbi decoding. We enrich SRL features with custom attributes that are custom made for this language. Our SRL system achieves F1 score of 78% in argument classification step on Croatian hr 500k corpus. In the second stage the proposed SRL model is used to develop AQG system for question generation from texts written in Croatian language. We proposed custom templates for AQG that were used to generate a total of 628 questions which were evaluated by experts scoring every question on a Likert scale. Expert evaluation of the system showed that our AQG achieved good results. The evaluation showed that 68% of the generated questions could be used for educational purposes. With these results the proposed AQG system could be used for possible implementation inside educational systems such as Intelligent Tutoring Systems

Resolving pronominal anaphora using commonsense knowledge

Coreference resolution is the task of resolving all expressions in a text that refer to the same entity. Such expressions are often used in writing and speech as shortcuts to avoid repetition. The most frequent form of coreference is the anaphor. To resolve anaphora not only grammatical and syntactical strategies are required, but also semantic approaches should be taken into consideration. This dissertation presents a framework for automatically resolving pronominal anaphora by integrating recent findings from the field of linguistics with new semantic features. Commonsense knowledge is the routine knowledge people have of the everyday world. Because such knowledge is widely used it is frequently omitted from social communications such as texts. It is understandable that without this knowledge computers will have difficulty making sense of textual information. In this dissertation a new set of computational and linguistic features are used in a supervised learning approach to resolve the pronominal anaphora in document. Commonsense knowledge sources such as ConceptNet and WordNet are used and similarity measures are extracted to uncover the elaborative information embedded in the words that can help in the process of anaphora resolution. The anaphoric system is tested on 350 Wall Street Journal articles from the BBN corpus. When compared with other systems available such as BART (Versley et al. 2008) and Charniak and Elsner 2009, our system performed better and also resolved a much wider range of anaphora. We were able to achieve a 92% F-measure on the BBN corpus and an average of 85% F-measure when tested on other genres of documents such as children stories and short stories selected from the web

Neural Graph Transfer Learning in Natural Language Processing Tasks

Natural language is essential in our daily lives as we rely on languages to communicate and exchange information. A fundamental goal for natural language processing (NLP) is to let the machine understand natural language to help or replace human experts to mine knowledge and complete tasks. Many NLP tasks deal with sequential data. For example, a sentence is considered as a sequence of works. Very recently, deep learning-based language models (i.e.,BERT \citep{devlin2018bert}) achieved significant improvement in many existing tasks, including text classification and natural language inference. However, not all tasks can be formulated using sequence models. Specifically, graph-structured data is also fundamental in NLP, including entity linking, entity classification, relation extraction, abstractive meaning representation, and knowledge graphs \citep{santoro2017simple,hamilton2017representation,kipf2016semi}. In this scenario, BERT-based pretrained models may not be suitable. Graph Convolutional Neural Network (GCN) \citep{kipf2016semi} is a deep neural network model designed for graphs. It has shown great potential in text classification, link prediction, question answering and so on. This dissertation presents novel graph models for NLP tasks, including text classification, prerequisite chain learning, and coreference resolution. We focus on different perspectives of graph convolutional network modeling: for text classification, a novel graph construction method is proposed which allows interpretability for the prediction; for prerequisite chain learning, we propose multiple aggregation functions that utilize neighbors for better information exchange; for coreference resolution, we study how graph pretraining can help when labeled data is limited. Moreover, an important branch is to apply pretrained language models for the mentioned tasks. So, this dissertation also focuses on the transfer learning method that generalizes pretrained models to other domains, including medical, cross-lingual, and web data. Finally, we propose a new task called unsupervised cross-domain prerequisite chain learning, and study novel graph-based methods to transfer knowledge over graphs

Towards Multilingual Coreference Resolution

The current work investigates the problems that occur when coreference resolution is considered as a multilingual task. We assess the issues that arise when a framework using the mention-pair coreference resolution model and memory-based learning for the resolution process are used. Along the way, we revise three essential subtasks of coreference resolution: mention detection, mention head detection and feature selection. For each of these aspects we propose various multilingual solutions including both heuristic, rule-based and machine learning methods. We carry out a detailed analysis that includes eight different languages (Arabic, Catalan, Chinese, Dutch, English, German, Italian and Spanish) for which datasets were provided by the only two multilingual shared tasks on coreference resolution held so far: SemEval-2 and CoNLL-2012. Our investigation shows that, although complex, the coreference resolution task can be targeted in a multilingual and even language independent way. We proposed machine learning methods for each of the subtasks that are affected by the transition, evaluated and compared them to the performance of rule-based and heuristic approaches. Our results confirmed that machine learning provides the needed flexibility for the multilingual task and that the minimal requirement for a language independent system is a part-of-speech annotation layer provided for each of the approached languages. We also showed that the performance of the system can be improved by introducing other layers of linguistic annotations, such as syntactic parses (in the form of either constituency or dependency parses), named entity information, predicate argument structure, etc. Additionally, we discuss the problems occurring in the proposed approaches and suggest possibilities for their improvement

Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets

Existing analysis work in machine reading comprehension (MRC) is largely concerned with evaluating the capabilities of systems. However, the capabilities of datasets are not assessed for benchmarking language understanding precisely. We propose a semi-automated, ablation-based methodology for this challenge; By checking whether questions can be solved even after removing features associated with a skill requisite for language understanding, we evaluate to what degree the questions do not require the skill. Experiments on 10 datasets (e.g., CoQA, SQuAD v2.0, and RACE) with a strong baseline model show that, for example, the relative scores of a baseline model provided with content words only and with shuffled sentence words in the context are on average 89.2% and 78.5% of the original score, respectively. These results suggest that most of the questions already answered correctly by the model do not necessarily require grammatical and complex reasoning. For precise benchmarking, MRC datasets will need to take extra care in their design to ensure that questions can correctly evaluate the intended skills.Comment: 11 pages, AAAI2020, with extra examples, data: https://github.com/Alab-NII/mrc-ablatio

Modeling Local Coherence: An Entity-Based Approach

This article proposes a novel framework for representing and measuring local coherence. Central to this approach is the entity-grid representation of discourse, which captures patterns of entity distribution in a text. The algorithm introduced in the article automatically abstracts a text into a set of entity transition sequences and records distributional, syntactic, and referential information about discourse entities. We re-conceptualize coherence assessment as a learning task and show that our entity-based representation is well-suited for ranking-based generation and text classification tasks. Using the proposed representation, we achieve good performance on text ordering, summary coherence evaluation, and readability assessment. 1