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

EVENT COREFERENCE RESOLUTION

Ph.DDOCTOR OF PHILOSOPH

Graph-based Patterns for Local Coherence Modeling

Coherence is an essential property of well-written texts. It distinguishes a multi-sentence text from a sequence of randomly strung sentences. The task of local coherence modeling is about the way that sentences in a text link up one another. Solving this task is beneficial for assessing the quality of texts. Moreover, a coherence model can be integrated into text generation systems such as text summarizers to produce coherent texts. In this dissertation, we present a graph-based approach to local coherence modeling that accounts for the connectivity structure among sentences in a text. Graphs give our model the capability to take into account relations between non-adjacent sentences as well as those between adjacent sentences. Besides, the connectivity style among nodes in graphs reflects the relationships among sentences in a text. We first employ the entity graph approach, proposed by Guinaudeau and Strube (2013), to represent a text via a graph. In the entity graph representation of a text, nodes encode sentences and edges depict the existence of a pair of coreferent mentions in sentences. We then devise graph-based features to capture the connectivity structure of nodes in a graph, and accordingly the connectivity structure of sentences in the corresponding text. We extract all subgraphs of entity graphs as features which encode the connectivity structure of graphs. Frequencies of subgraphs correlate with the perceived coherence of their corresponding texts. Therefore, we refer to these subgraphs as coherence patterns. In order to complete our approach to coherence modeling, we propose a new graph representation of texts, rather than the entity graph. Our approach employs lexico-semantic relations among words in sentences, instead of only entity coreference relations, to model relationships between sentences via a graph. This new lexical graph representation of text plus our method for mining coherence patterns make our coherence model. We evaluate our approach on the readability assessment task because a primary factor of readability is coherence. Coherent texts are easy to read and consequently demand less effort from their readers. Our extensive experiments on two separate readability assessment datasets show that frequencies of coherence patterns in texts correlate with the readability ratings assigned by human judges. By training a machine learning method on our coherence patterns, our model outperforms its counterparts on ranking texts with respect to their readability. As one of the ultimate goals of coherence models is to be used in text generation systems, we show how our coherence patterns can be integrated into a graph-based text summarizer to produce informative and coherent summaries. Our coherence patterns improve the performance of the summarization system based on both standard summarization metrics and human evaluations. An implementation of the approaches discussed in this dissertation is publicly available

Structured Representations for Coreference Resolution

Coreference resolution is the task of determining which expressions in a text are used to refer to the same entity. This task is one of the most fundamental problems of natural language understanding. Inherently, coreference resolution is a structured task, as the output consists of sets of coreferring expressions. This complex structure poses several challenges since it is not clear how to account for the structure in terms of error analysis and representation. In this thesis, we present a treatment of computational coreference resolution that accounts for the structure. Our treatment encompasses error analysis and the representation of approaches to coreference resolution. In particular, we propose two frameworks in this thesis. The first framework deals with error analysis. We gather requirements for an appropriate error analysis method and devise a framework that considers a structured graph-based representation of the reference annotation and the system output. Error extraction is performed by constructing linguistically motivated or data-driven spanning trees for the graph-based coreference representations. The second framework concerns the representation of approaches to coreference resolution. We show that approaches to coreference resolution can be understood as predictors of latent structures that are not annotated in the data. From these latent structures, the final output is derived during a post-processing step. We devise a machine learning framework for coreference resolution based on this insight. In this framework, we have a unified representation of approaches to coreference resolution. Individual approaches can be expressed as instantiations of a generic approach. We express many approaches from the literature as well as novel variants in our framework, ranging from simple pairwise classification approaches to complex entity-centric models. Using the uniform representation, we are able to analyze differences and similarities between the models transparently and in detail. Finally, we employ the error analysis framework to perform a qualitative analysis of differences in error profiles of the models on a benchmark dataset. We trace back differences in the error profiles to differences in the representation. Our analysis shows that a mention ranking model and a tree-based mention-entity model with left-to-right inference have the highest performance. We discuss reasons for the improved performance and analyze why more advanced approaches modeled in our framework cannot improve on these models. An implementation of the frameworks discussed in this thesis is publicly available

Schema-learning and rebinding as mechanisms of in-context learning and emergence

In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs). Yet the mechanisms that underlie it are poorly understood. In this paper, we demonstrate that comparable ICL capabilities can be acquired by an alternative sequence prediction learning method using clone-structured causal graphs (CSCGs). Moreover, a key property of CSCGs is that, unlike transformer-based LLMs, they are {\em interpretable}, which considerably simplifies the task of explaining how ICL works. Specifically, we show that it uses a combination of (a) learning template (schema) circuits for pattern completion, (b) retrieving relevant templates in a context-sensitive manner, and (c) rebinding of novel tokens to appropriate slots in the templates. We go on to marshall evidence for the hypothesis that similar mechanisms underlie ICL in LLMs. For example, we find that, with CSCGs as with LLMs, different capabilities emerge at different levels of overparameterization, suggesting that overparameterization helps in learning more complex template (schema) circuits. By showing how ICL can be achieved with small models and datasets, we open up a path to novel architectures, and take a vital step towards a more general understanding of the mechanics behind this important capability