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)

CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania

CLIFF is the Computational Linguists\u27 Feedback Forum. We are a group of students and faculty who gather once a week to hear a presentation and discuss work currently in progress. The \u27feedback\u27 in the group\u27s name is important: we are interested in sharing ideas, in discussing ongoing research, and in bringing together work done by the students and faculty in Computer Science and other departments. However, there are only so many presentations which we can have in a year. We felt that it would be beneficial to have a report which would have, in one place, short descriptions of the work in Natural Language Processing at the University of Pennsylvania. This report then, is a collection of abstracts from both faculty and graduate students, in Computer Science, Psychology and Linguistics. We want to stress the close ties between these groups, as one of the things that we pride ourselves on here at Penn is the communication among different departments and the inter-departmental work. Rather than try to summarize the varied work currently underway at Penn, we suggest reading the abstracts to see how the students and faculty themselves describe their work. The report illustrates the diversity of interests among the researchers here, as well as explaining the areas of common interest. In addition, since it was our intent to put together a document that would be useful both inside and outside of the university, we hope that this report will explain to everyone some of what we are about

Presupposition Resolution With Discourse Information Structures

An approach to resolving a number of presuppositional phenomena, including definite descriptions and pronominal anaphora, is described within the larger context of an architecture for query-based, task-oriented human/computer dialogue. The model of discourse context employed assumes that discourse structure is organized around a stack of questions under discussion, which plays an important role in narrowing the search space for referents and other presupposed information. The algorithms of individual presuppositional operators for maintaining discourse structures are presented and illustrated in several example dialogues in which human users interact with an agent in order to make hotel reservations. The overall architecture is compared to SDRT (Segmented Discourse Representation Theory), in terms of efficiency and ease of implementation

Discourse structure and information structure : interfaces and prosodic realization

In this paper we review the current state of research on the issue of discourse structure (DS) / information structure (IS) interface. This field has received a lot of attention from discourse semanticists and pragmatists, and has made substantial progress in recent years. In this paper we summarize the relevant studies. In addition, we look at the issue of DS/ISinteraction at a different level—that of phonetics. It is known that both information structure and discourse structure can be realized prosodically, but the issue of phonetic interaction between the prosodic devices they employ has hardly ever been discussed in this context. We think that a proper consideration of this aspect of DS/IS-interaction would enrich our understanding of the phenomenon, and hence we formulate some related research-programmatic positions

CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania

The Computational Linguistics Feedback Forum (CLIFF) is a group of students and faculty who gather once a week to discuss the members\u27 current research. As the word feedback suggests, the group\u27s purpose is the sharing of ideas. The group also promotes interdisciplinary contacts between researchers who share an interest in Cognitive Science. There is no single theme describing the research in Natural Language Processing at Penn. There is work done in CCG, Tree adjoining grammars, intonation, statistical methods, plan inference, instruction understanding, incremental interpretation, language acquisition, syntactic parsing, causal reasoning, free word order languages, ... and many other areas. With this in mind, rather than trying to summarize the varied work currently underway here at Penn, we suggest reading the following abstracts to see how the students and faculty themselves describe their work. Their abstracts illustrate the diversity of interests among the researchers, explain the areas of common interest, and describe some very interesting work in Cognitive Science. This report is a collection of abstracts from both faculty and graduate students in Computer Science, Psychology and Linguistics. We pride ourselves on the close working relations between these groups, as we believe that the communication among the different departments and the ongoing inter-departmental research not only improves the quality of our work, but makes much of that work possible

Abductive speech act recognition, corporate agents and the COSMA system

This chapter presents an overview of the DISCO project\u27s solutions to several problems in natural language pragmatics. Its central focus is on relating utterances to intentions through speech act recognition. Subproblems include the incorporation of linguistic cues into the speech act recognition process, precise and efficient multiagent belief attribution models (Corporate Agents), and speech act representation and processing using Corporate Agents. These ideas are being tested within the COSMA appointment scheduling system, one application of the DISCO natural language interface. Abductive speech act processing in this environment is not far from realizing its potential for fully bidirectional implementation

5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair

Providing voice assistants the ability to navigate multi-turn conversations is a challenging problem. Handling multi-turn interactions requires the system to understand various conversational use-cases, such as steering, intent carryover, disfluencies, entity carryover, and repair. The complexity of this problem is compounded by the fact that these use-cases mix with each other, often appearing simultaneously in natural language. This work proposes a non-autoregressive query rewriting architecture that can handle not only the five aforementioned tasks, but also complex compositions of these use-cases. We show that our proposed model has competitive single task performance compared to the baseline approach, and even outperforms a fine-tuned T5 model in use-case compositions, despite being 15 times smaller in parameters and 25 times faster in latency.Comment: Interspeech 202

The significance of silence. Long gaps attenuate the preference for ‘yes’ responses in conversation.

In conversation, negative responses to invitations, requests, offers and the like more often occur with a delay – conversation analysts talk of them as dispreferred. Here we examine the contrastive cognitive load ‘yes’ and ‘no’ responses make, either when given relatively fast (300 ms) or delayed (1000 ms). Participants heard minidialogues, with turns extracted from a spoken corpus, while having their EEG recorded. We find that a fast ‘no’ evokes an N400-effect relative to a fast ‘yes’, however this contrast is not present for delayed responses. This shows that an immediate response is expected to be positive – but this expectation disappears as the response time lengthens because now in ordinary conversation the probability of a ‘no’ has increased. Additionally, however, 'No' responses elicit a late frontal positivity both when they are fast and when they are delayed. Thus, regardless of the latency of response, a ‘no’ response is associated with a late positivity, since a negative response is always dispreferred and may require an account. Together these results show that negative responses to social actions exact a higher cognitive load, but especially when least expected, as an immediate response