Seminars

Jul
7
Mon
Distinguishing “possible” from “probable” meaning shifts: How distributions impact linguistic theory – James Pustejovsky (Brandeis University) @ Czech Republic
Jul 7 @ 7:30 pm – 8:30 pm

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Abstract
In this talk, I discuss the changing role of data in modeling natural language, as captured in linguistic theories. The generative tradition of introducing data using only “evaluation procedures”, rather than “discovery procedures”, promoted by Chomsky in the 1950s, is slowly being unraveled by the exploitation of significant language datasets that were unthinkable in the 1960s. Evaluation procedures focus on possible generative devices in language without constraints from actual (probable) occurrences of the constructions. After showing how both procedures are natural to scientific inquiry, I describe the natural tension between data and the theory that aims to model it, with specific reference to the nature of the lexicon and semantic selection. The seeming chaos of organic data inevitably violates our theoretical assumptions. But in the end, it is restrictions apparent in the data that call for postulating structure within a revised theoretical model.
All PRELIM Seminars will be held in Room S9, 1st Floor.

Jul
8
Tue
A Rich Probabilistic Type Theory for the Semantics of Natural Language – Shalom Lappin (King’s College London) @ Czech Republic
Jul 8 @ 3:00 pm – 4:00 pm

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Abstract
In a classical semantic theory meaning is defined in terms of truth conditions. The meaning of a sentence is built up compositionally through a sequence of functions from the semantic values of constituent expressions to the value of the expression formed from these syntactic elements (Montague, 1974). In this framework the type system is categorical. A type T identifies a set of possible denotations for expressions from the values of its constituents.

There are at least two problems with this framework. First, it cannot represent the gradience of semantic properties that is pervasive in speakers’ judgements concerning truth, predication, and meaning relations. Second, it offers no account of semantic learning. It is not clear how a reasonable account of semantic learning could be constructed on the basis of the categorical type systems that a classical semantic theory assumes. Such a system does not appear to be efficiently learnable from the primary linguistic data (with weak learning biases), nor is there much psychological data to suggest that it expresses biologically determined constraints on semantic learning.

A semantic theory that assigns probability rather than truth conditions to sentences is in a better position to deal with both of these issues. Gradience is intrinsic to the theory by virtue of the fact that speakers assign values to declarative sentences in the continuum of real numbers [0,1], rather than Boolean values in {0,1}. Moreover, a probabilistic account of semantic learning is facilitated if the target of learning is a probabilistic representation of meaning.

We consider two strategies for constructing a probabilistic semantics. One is a top-down approach where one sustains classical (categorical) type and model theories, and then specifies a function that assigns probability values to the possible worlds that the model provides. The probability value of a sentence relative to a model M is the sum of the probabilities of the worlds in which it is true. The other is a bottom-up approach where one defines a probabilistic type theory and characterizes the probability value of an Austinian proposition relative to a set of situation types (Cooper (2012)). This proposition is the output of the function that applies to the probabilistic semantic type judgements associated with the syntactic constituents of the proposition.

All PRELIM Seminars will be held in Room S9, 1st Floor.

Semantics, Science, and 10-year Olds – Oren Etzioni (University of Washington, Allen Institute) @ Czech Republic
Jul 8 @ 7:30 pm – 8:30 pm

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Abstract
The Allen Institute of AI (AI2) has is building software that aims to achieve proficiency on standardized science & math tests. My talk will introduce AI2, its research methodology, and describe a series of semantic challenges, and associated data sets that we are sharing with the community. Unlike other speakers, I’m offering problems not solutions.
All PRELIM Seminars will be held in Room S9, 1st Floor.

Jul
9
Wed
Bayesian Pragmatics – Dan Lassiter (Stanford University) @ Czech Republic
Jul 9 @ 3:00 pm – 4:00 pm

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Abstract
An influential body of work in recent cognitive science makes use of structured Bayesian models to understand diverse cognitive activities in which uncertainty plays a critical role, such as reasoning, vision, learning, social cognition, and syntactic processing. Similar problems arise in semantics and pragmatics, where ambiguity, vagueness, and context-sensitivity are commonplace, and rich pragmatic interpretation are massively underdetermined by the linguistic signal. In recent work Noah Goodman and I have developed a framework combining structured Bayesian models with compositional model-theoretic semantics, incorporating insights from Gricean and game-theoretic pragmatics. We have argued that this gives new insight into how and why context and background knowledge influence interpretation. I will describe the approach at a high level and consider three test cases: (1) predictions about the effects of background expectations on ambiguity resolution, (2) how the model derives graded implicatures as probabilistic inferences about speaker intentions, and (3) how context influences the information conveyed by vague and context-sensitive language.

All PRELIM Seminars will be held in Room S9, 1st Floor.

The Problem of Reference – David McAllester (Toyota Technical Institute) @ Czech Republic
Jul 9 @ 7:30 pm – 8:30 pm

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Abstract
This talk will present an approach to the semantics of natural language focusing on the problem of reference. Phrases of natural language refer to things in the world such as “Obama”, “Air Force One”, “the disappeared Malaysian airliner” or “the annexation of Crimea”. Sampled sentences will be used to argue that resolving reference is essential to any treatment of semantics. The discussion of natural language semantics will include both philosophical considerations, such as the notion of “a thing in the world” and the problem of grounding, as well as concrete engineering problems such as achieving good performance on the CoNLL 2012 shared task coreference evaluation. A new grammar formalism for modeling reference — entity grammars — will also be presented.
All PRELIM Seminars will be held in Room S9, 1st Floor.

Jul
10
Thu
Perceptual Semantics and Coordination in Dialogue – Staffan Larsson (University of Gothenburg) @ Czech Republic
Jul 10 @ 5:00 am – 4:00 pm

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Abstract
A formal semantics for low-level perceptual aspects of meaning is presented, tying these together with the logical-inferential aspects of meaning traditionally studied in formal semantics. The key idea is to model perceptual meanings as classifiers of perceptual input. Furthermore, we show how perceptual aspects of meaning can be updated as a result of observing language use in interaction, thereby enabling fine-grained semantic plasticity and semantic coordination. This requires a framework where intensions are (1) represented independently of extensions, and (2) structured objects which can be modified as a result of learning. We use Type Theory with Records (TTR), a formal semantics framework which starts from the idea that information and meaning is founded on our ability to perceive and classify the world, i.e., to perceive objects and situations as being of types. As an example of our approach, we show how a simple classifier of spatial information based on the Perceptron can be cast in TTR. Time permitting, we will also outline preliminary accounts of compositionality and vagueness of perceptual meanings, the latter using probabilistic TTR.
All PRELIM Seminars will be held in Room S9, 1st Floor.

The State of the Art in Semantic Parsing – Percy Liang (Stanford University) @ Czech Republic
Jul 10 @ 7:30 pm – 8:30 pm

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Abstract
Semantic parsing, the task of mapping utterances to semantic representations (e.g. logical forms), has its roots in the early natural language understanding systems of the 1960s. These rule-based systems were representationally sophisticated, but brittle, and thus fell out of favor as the statistical revolution swept NLP. Since the late 1990s, however, there has been a resurgence of interest in semantic parsing from the statistical perspective, where the representations are logical but the learning is not. Most recently, there are efforts to learn these logical forms automatically from denotations, a much more realistic but also challenging setting. The learning perspective has both led to practical large-scale semantic parsers, but interestingly also has implications for the semantic representations.
All PRELIM Seminars will be held in Room S9, 1st Floor.

Jul
11
Fri
Designing Abstract Meaning Representations for Machine Translation – Martha Palmer (University of Colorado) @ Czech Republic
Jul 11 @ 3:00 pm – 4:00 pm

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Abstract
Abstract Meaning Representations (AMRs) are rooted, directional and labeled graphs that abstract away from morpho-syntactic idiosyncrasies such as word category (verbs and nouns), word order, and function words (determiners, some prepositions). They also make explicit many implicit semantic and pragmatic relations. Because syntactic idiosyncrasies and different choices about what to make explicit account for many cross-lingual differences, it is worth exploring whether this representation can serve as a useful, minimally divergent transfer layer in machine translation. This talk will present some of the challenges of semantic representations and discuss the contributions AMRs provide over and above other current representation schemes.

All PRELIM Seminars will be held in Room S9, 1st Floor.

Common Sense and Language – Benjamin van Durme (Johns Hopkins University) @ Czech Republic
Jul 11 @ 7:30 pm – 8:30 pm

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2014 Frederick Jelinek Memorial Summer Workshop

Abstract
It is widely assumed that natural language understanding requires a significant amount of general world knowledge, or ‘common sense’. I will first review various expressions of this claim, and define common sense (Common Sense for Language). I then will describe two approaches to automatically acquiring this knowledge, Common Sense from Language, either from the generalization over multiple situational descriptions, or in the direct interpretation of generic sentences. I will claim that both lead to the same roadblock: we can acquire common sense in the form of generic-like statements, but standard text corpora on their own do not easily, explicitly relay the underlying quantifier domain restrictions, nor quantifier strengths, that are required for full generic interpretation. Moving from a ‘possible’ to a ‘probable’ interpretation of generics is then the major obstacle in acquiring general world knowledge for NLU (if we wish to rely exclusively on text-based acquisition).

All PRELIM Seminars will be held in Room S9, 1st Floor.

Center for Language and Speech Processing