Ellipsis Resolution as Question Answering: An Evaluation

Most, if not all forms of ellipsis (e.g., so does Mary) are similar to reading comprehension questions (what does Mary do), in that in order to resolve them, we need to identify an appropriate text span in the preceding discourse. Following this observation, we present an alternative approach for English ellipsis resolution relying on architectures developed for question answering (QA). We present both single-task models, and joint models trained on auxiliary QA and coreference resolution datasets, clearly outperforming the current state of the art for Sluice Ellipsis (from 70.00 to 86.01 F1) and Verb Phrase Ellipsis (from 72.89 to 78.66 F1).Comment: To appear in EACL 202

Model-based annotation of coreference

Humans do not make inferences over texts, but over models of what texts are about. When annotators are asked to annotate coreferent spans of text, it is therefore a somewhat unnatural task. This paper presents an alternative in which we preprocess documents, linking entities to a knowledge base, and turn the coreference annotation task -- in our case limited to pronouns -- into an annotation task where annotators are asked to assign pronouns to entities. Model-based annotation is shown to lead to faster annotation and higher inter-annotator agreement, and we argue that it also opens up for an alternative approach to coreference resolution. We present two new coreference benchmark datasets, for English Wikipedia and English teacher-student dialogues, and evaluate state-of-the-art coreference resolvers on them.Comment: To appear in LREC 202

Realistic Dialogue Engine for Video Games

The concept of believable agent has a long history in Artificial Intelligence. It has applicability in multiple fields, particularly video games. Video games have shown tremendous technological advancement in several areas such as graphics and music; however, techniques used to simulate dialogue are still quite outdated. In this thesis, a method is proposed to allow a human player to interact with non-player characters using natural-language input. By using various techniques of modern Artificial Intelligence such as information retrieval and sentiment analysis, non-player characters have the capability of engaging in dynamic dialogue: they can answer questions, ask questions, remember events, and more. This conversation system is highly customizable, so the types of responses that non-player characters give can be modified to fit within a game’s storyline. Although the system only currently allows for simple dialogue, it illustrates the potential for a more robust way to simulate believable agents in video games

An authoring tool for decision support systems in context questions of ecological knowledge

Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.This paper has been partially supported by the MESOLAP (TIN2010-14860), GEODAS-BI (TIN2012-37493-C03-03), LEGOLANGUAGE (TIN2012-31224) and DIIM2.0 (PROMETEOII/2014/001) projects from the Spanish Ministry of Education and Competitivity. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)

Anaphora Resolution and Text Retrieval

Empirical approaches based on qualitative or quantitative methods of corpus linguistics have become a central paradigm within linguistics. The series takes account of this fact and provides a platform for approaches within synchronous linguistics as well as interdisciplinary works with a linguistic focus which devise new ways of working empirically and develop new data-based methods and theoretical models for empirical linguistic analyses

Anaphora Resolution and Text Retrieval

Empirical approaches based on qualitative or quantitative methods of corpus linguistics have become a central paradigm within linguistics. The series takes account of this fact and provides a platform for approaches within synchronous linguistics as well as interdisciplinary works with a linguistic focus which devise new ways of working empirically and develop new data-based methods and theoretical models for empirical linguistic analyses