16,891 research outputs found
Computational Approaches to the Pragmatics Problem
Cummins C, de Ruiter J. Computational Approaches to the Pragmatics Problem. Language and Linguistics Compass. 2014;8(4):133-143.Unlike many aspects of human language, pragmatics involves a systematic many-to-many mapping between form and meaning. This renders the computational problems of encoding and decoding meaning especially challenging, both for humans in normal conversation and for artificial dialogue systems that need to understand their users' input. A particularly striking example of this difficulty is the recognition of speech act or dialogue act types. In this review, we discuss why this is a problem and why its solution is potentially relevant both for our understanding of human interaction and for the implementation of artificial systems. We examine some of the theoretical and practical attempts that have been made to overcome this problem and consider how the field might develop in the near future
Teaching and learning guide for:Computational approaches to the pragmatics problem
Cummins C, de Ruiter J. Teaching And Learning Guide For Computational Approaches To The Pragmatics Problem. Language and Linguistics Compass. 2015;9(8):328-331
Reasoning About Pragmatics with Neural Listeners and Speakers
We present a model for pragmatically describing scenes, in which contrastive
behavior results from a combination of inference-driven pragmatics and learned
semantics. Like previous learned approaches to language generation, our model
uses a simple feature-driven architecture (here a pair of neural "listener" and
"speaker" models) to ground language in the world. Like inference-driven
approaches to pragmatics, our model actively reasons about listener behavior
when selecting utterances. For training, our approach requires only ordinary
captions, annotated _without_ demonstration of the pragmatic behavior the model
ultimately exhibits. In human evaluations on a referring expression game, our
approach succeeds 81% of the time, compared to a 69% success rate using
existing techniques
Unified Pragmatic Models for Generating and Following Instructions
We show that explicit pragmatic inference aids in correctly generating and
following natural language instructions for complex, sequential tasks. Our
pragmatics-enabled models reason about why speakers produce certain
instructions, and about how listeners will react upon hearing them. Like
previous pragmatic models, we use learned base listener and speaker models to
build a pragmatic speaker that uses the base listener to simulate the
interpretation of candidate descriptions, and a pragmatic listener that reasons
counterfactually about alternative descriptions. We extend these models to
tasks with sequential structure. Evaluation of language generation and
interpretation shows that pragmatic inference improves state-of-the-art
listener models (at correctly interpreting human instructions) and speaker
models (at producing instructions correctly interpreted by humans) in diverse
settings.Comment: NAACL 2018, camera-ready versio
On an Intuitionistic Logic for Pragmatics
We reconsider the pragmatic interpretation of intuitionistic logic [21]
regarded as a logic of assertions and their justications and its relations with classical
logic. We recall an extension of this approach to a logic dealing with assertions
and obligations, related by a notion of causal implication [14, 45]. We focus on
the extension to co-intuitionistic logic, seen as a logic of hypotheses [8, 9, 13] and on
polarized bi-intuitionistic logic as a logic of assertions and conjectures: looking at the
S4 modal translation, we give a denition of a system AHL of bi-intuitionistic logic
that correctly represents the duality between intuitionistic and co-intuitionistic logic,
correcting a mistake in previous work [7, 10]. A computational interpretation of cointuitionism
as a distributed calculus of coroutines is then used to give an operational
interpretation of subtraction.Work on linear co-intuitionism is then recalled, a linear
calculus of co-intuitionistic coroutines is dened and a probabilistic interpretation
of linear co-intuitionism is given as in [9]. Also we remark that by extending the
language of intuitionistic logic we can express the notion of expectation, an assertion
that in all situations the truth of p is possible and that in a logic of expectations
the law of double negation holds. Similarly, extending co-intuitionistic logic, we can
express the notion of conjecture that p, dened as a hypothesis that in some situation
the truth of p is epistemically necessary
A preliminary bibliography on focus
[I]n its present form, the bibliography contains approximately 1100 entries. Bibliographical work is never complete, and the present one is still modest in a number of respects. It is not annotated, and it still contains a lot of mistakes and inconsistencies. It has nevertheless reached a stage which justifies considering the possibility of making it available to the public. The first step towards this is its pre-publication in the form of this working paper. […]
The bibliography is less complete for earlier years. For works before 1970, the bibliographies of Firbas and Golkova 1975 and Tyl 1970 may be consulted, which have not been included here
Cross-Lingual Adaptation using Structural Correspondence Learning
Cross-lingual adaptation, a special case of domain adaptation, refers to the
transfer of classification knowledge between two languages. In this article we
describe an extension of Structural Correspondence Learning (SCL), a recently
proposed algorithm for domain adaptation, for cross-lingual adaptation. The
proposed method uses unlabeled documents from both languages, along with a word
translation oracle, to induce cross-lingual feature correspondences. From these
correspondences a cross-lingual representation is created that enables the
transfer of classification knowledge from the source to the target language.
The main advantages of this approach over other approaches are its resource
efficiency and task specificity.
We conduct experiments in the area of cross-language topic and sentiment
classification involving English as source language and German, French, and
Japanese as target languages. The results show a significant improvement of the
proposed method over a machine translation baseline, reducing the relative
error due to cross-lingual adaptation by an average of 30% (topic
classification) and 59% (sentiment classification). We further report on
empirical analyses that reveal insights into the use of unlabeled data, the
sensitivity with respect to important hyperparameters, and the nature of the
induced cross-lingual correspondences
- …