160 research outputs found
Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text
Modeling semantic plausibility requires commonsense knowledge about the world
and has been used as a testbed for exploring various knowledge representations.
Previous work has focused specifically on modeling physical plausibility and
shown that distributional methods fail when tested in a supervised setting. At
the same time, distributional models, namely large pretrained language models,
have led to improved results for many natural language understanding tasks. In
this work, we show that these pretrained language models are in fact effective
at modeling physical plausibility in the supervised setting. We therefore
present the more difficult problem of learning to model physical plausibility
directly from text. We create a training set by extracting attested events from
a large corpus, and we provide a baseline for training on these attested events
in a self-supervised manner and testing on a physical plausibility task. We
believe results could be further improved by injecting explicit commonsense
knowledge into a distributional model.Comment: Accepted at COIN@EMNLP 201
Multimodal Event Knowledge in Online Sentence Comprehension: The Influence of Visual Context on Anticipatory Eye Movements
People predict incoming words during online sentence comprehension based on their knowledge of real-world events that is cued by preceding linguistic contexts. We used the visual world paradigm to investigate how event knowledge activated by an agent-verb pair is integrated with perceptual information about the referent that fits the patient role. During the verb time window participants looked significantly more at the referents that are expected given the agent-verb pair. Results are consistent with the assumption that event-based knowledge involves perceptual properties of typical participants. The knowledge activated by the agent is compositionally integrated with knowledge cued by the verb to drive anticipatory eye movements during sentence comprehension based on the expectations associated not only with the incoming word, but also with the visual features of its referent
From Verbs to Tasks: An Integrated Account of Learning Tasks from Situated Interactive Instruction.
Intelligent collaborative agents are becoming common in the human society. From virtual assistants such as Siri and Google Now to assistive robots, they contribute to human activities in a variety of ways. As they become more pervasive, the challenge of customizing them to a variety of environments and tasks becomes critical. It is infeasible for engineers to program them for each individual use. Our research aims at building interactive robots and agents that adapt to new environments autonomously by interacting with human users using natural modalities.
This dissertation studies the problem of learning novel tasks from human-agent dialog. We propose a novel approach for interactive task learning, situated interactive instruction (SII), and investigate approaches to three computational challenges that arise in designing SII agents: situated comprehension, mixed-initiative interaction, and interactive task learning. We propose a novel mixed-modality grounded representation for task verbs which encompasses their lexical, semantic, and
task-oriented aspects. This representation is useful in situated comprehension and can be learned through human-agent interactions. We introduce the Indexical Model of comprehension that can exploit
extra-linguistic contexts for resolving semantic ambiguities in situated comprehension of task commands. The Indexical model is integrated with a mixed-initiative interaction model that facilitates
a flexible task-oriented human-agent dialog. This dialog serves as the basis of interactive task learning. We propose an interactive variation of explanation-based learning that can acquire the proposed
representation. We demonstrate that our learning paradigm is efficient, can transfer knowledge between structurally similar tasks, integrates agent-driven exploration with instructional learning, and can acquire several tasks. The methods proposed in this thesis are integrated in Rosie - a generally instructable agent developed in the Soar cognitive architecture and embodied on a table-top robot.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111573/1/shiwali_1.pd
Syntactic and semantic features for statistical and neural machine translation
Machine Translation (MT) for language pairs with long distance dependencies and
word reordering, such as GermanâEnglish, is prone to producing output that is lexically
or syntactically incoherent. Statistical MT (SMT) models used explicit or latent
syntax to improve reordering, however failed at capturing other long distance dependencies.
This thesis explores how explicit sentence-level syntactic information can improve
translation for such complex linguistic phenomena. In particular, we work at the
level of the syntactic-semantic interface with representations conveying the predicate-argument
structures. These are essential to preserving semantics in translation and
SMT systems have long struggled to model them.
String-to-tree SMT systems use explicit target syntax to handle long-distance reordering,
but make strong independence assumptions which lead to inconsistent lexical
choices. To address this, we propose a Selectional Preferences feature which models
the semantic affinities between target predicates and their argument fillers using the
target dependency relations available in the decoder. We found that our feature is not
effective in a string-to-tree system for GermanâEnglish and that often the conditioning
context is wrong because of mistranslated verbs.
To improve verb translation, we proposed a Neural Verb Lexicon Model (NVLM)
incorporating sentence-level syntactic context from the source which carries relevant
semantic information for verb disambiguation. When used as an extra feature for re-ranking
the output of a Germanâ English string-to-tree system, the NVLM improved
verb translation precision by up to 2.7% and recall by up to 7.4%.
While the NVLM improved some aspects of translation, other syntactic and lexical
inconsistencies are not being addressed by a linear combination of independent models.
In contrast to SMT, neural machine translation (NMT) avoids strong independence
assumptions thus generating more fluent translations and capturing some long-distance
dependencies. Still, incorporating additional linguistic information can improve translation
quality.
We proposed a method for tightly coupling target words and syntax in the NMT
decoder. To represent syntax explicitly, we used CCG supertags, which encode subcategorization
information, capturing long distance dependencies and attachments. Our
method improved translation quality on several difficult linguistic constructs, including
prepositional phrases which are the most frequent type of predicate arguments. These
improvements over a strong baseline NMT system were consistent across two language
pairs: 0.9 BLEU for GermanâEnglish and 1.2 BLEU for RomanianâEnglish
Syntax with oscillators and energy levels
This book presents a new approach to studying the syntax of human language, one which emphasizes how we think about time. Tilsen argues that many current theories are unsatisfactory because those theories conceptualize syntactic patterns with spatially arranged structures of objects. These object-structures are atemporal and do not lend well to reasoning about time. The book develops an alternative conceptual model in which oscillatory systems of various types interact with each other through coupling forces, and in which the relative energies of those systems are organized in particular ways. Tilsen emphasizes that the two primary mechanisms of the approach â oscillators and energy levels â require alternative ways of thinking about time. Furthermore, his theory leads to a new way of thinking about grammaticality and the recursive nature of language. The theory is applied to a variety of syntactic phenomena: word order, phrase structure, morphosyntax, constituency, case systems, ellipsis, anaphora, and islands. The book also presents a general program for the study of language in which the construction of linguistic theories is itself an object of theoretical analysis.
Reviewed by John Goldsmith, Mark Gibson and an anonymous reviewer. Signed reports are openly available in the downloads session
Syntax with oscillators and energy levels
This book presents a new approach to studying the syntax of human language, one which emphasizes how we think about time. Tilsen argues that many current theories are unsatisfactory because those theories conceptualize syntactic patterns with spatially arranged structures of objects. These object-structures are atemporal and do not lend well to reasoning about time. The book develops an alternative conceptual model in which oscillatory systems of various types interact with each other through coupling forces, and in which the relative energies of those systems are organized in particular ways. Tilsen emphasizes that the two primary mechanisms of the approach â oscillators and energy levels â require alternative ways of thinking about time. Furthermore, his theory leads to a new way of thinking about grammaticality and the recursive nature of language. The theory is applied to a variety of syntactic phenomena: word order, phrase structure, morphosyntax, constituency, case systems, ellipsis, anaphora, and islands. The book also presents a general program for the study of language in which the construction of linguistic theories is itself an object of theoretical analysis.
Reviewed by John Goldsmith, Mark Gibson and an anonymous reviewer. Signed reports are openly available in the downloads session
Syntax with oscillators and energy levels
This book presents a new approach to studying the syntax of human language, one which emphasizes how we think about time. Tilsen argues that many current theories are unsatisfactory because those theories conceptualize syntactic patterns with spatially arranged structures of objects. These object-structures are atemporal and do not lend well to reasoning about time. The book develops an alternative conceptual model in which oscillatory systems of various types interact with each other through coupling forces, and in which the relative energies of those systems are organized in particular ways. Tilsen emphasizes that the two primary mechanisms of the approach â oscillators and energy levels â require alternative ways of thinking about time. Furthermore, his theory leads to a new way of thinking about grammaticality and the recursive nature of language. The theory is applied to a variety of syntactic phenomena: word order, phrase structure, morphosyntax, constituency, case systems, ellipsis, anaphora, and islands. The book also presents a general program for the study of language in which the construction of linguistic theories is itself an object of theoretical analysis.
Reviewed by John Goldsmith, Mark Gibson and an anonymous reviewer. Signed reports are openly available in the downloads session
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