9,916 research outputs found
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
We propose a computational model of situated language comprehension based on
the Indexical Hypothesis that generates meaning representations by translating
amodal linguistic symbols to modal representations of beliefs, knowledge, and
experience external to the linguistic system. This Indexical Model incorporates
multiple information sources, including perceptions, domain knowledge, and
short-term and long-term experiences during comprehension. We show that
exploiting diverse information sources can alleviate ambiguities that arise
from contextual use of underspecific referring expressions and unexpressed
argument alternations of verbs. The model is being used to support linguistic
interactions in Rosie, an agent implemented in Soar that learns from
instruction.Comment: Advances in Cognitive Systems 3 (2014
On staying grounded and avoiding Quixotic dead ends
The 15 articles in this special issue on The Representation of Concepts illustrate the rich variety of theoretical positions and supporting research that characterize the area. Although much agreement exists among contributors, much disagreement exists as well, especially about the roles of grounding and abstraction in conceptual processing. I first review theoretical approaches raised in these articles that I believe are Quixotic dead ends, namely, approaches that are principled and inspired but likely to fail. In the process, I review various theories of amodal symbols, their distortions of grounded theories, and fallacies in the evidence used to support them. Incorporating further contributions across articles, I then sketch a theoretical approach that I believe is likely to be successful, which includes grounding, abstraction, flexibility, explaining classic conceptual phenomena, and making contact with real-world situations. This account further proposes that (1) a key element of grounding is neural reuse, (2) abstraction takes the forms of multimodal compression, distilled abstraction, and distributed linguistic representation (but not amodal symbols), and (3) flexible context-dependent representations are a hallmark of conceptual processing
What does semantic tiling of the cortex tell us about semantics?
Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) features and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions
Language with Vision: a Study on Grounded Word and Sentence Embeddings
Grounding language in vision is an active field of research seeking to
construct cognitively plausible word and sentence representations by
incorporating perceptual knowledge from vision into text-based representations.
Despite many attempts at language grounding, achieving an optimal equilibrium
between textual representations of the language and our embodied experiences
remains an open field. Some common concerns are the following. Is visual
grounding advantageous for abstract words, or is its effectiveness restricted
to concrete words? What is the optimal way of bridging the gap between text and
vision? To what extent is perceptual knowledge from images advantageous for
acquiring high-quality embeddings? Leveraging the current advances in machine
learning and natural language processing, the present study addresses these
questions by proposing a simple yet very effective computational grounding
model for pre-trained word embeddings. Our model effectively balances the
interplay between language and vision by aligning textual embeddings with
visual information while simultaneously preserving the distributional
statistics that characterize word usage in text corpora. By applying a learned
alignment, we are able to indirectly ground unseen words including abstract
words. A series of evaluations on a range of behavioural datasets shows that
visual grounding is beneficial not only for concrete words but also for
abstract words, lending support to the indirect theory of abstract concepts.
Moreover, our approach offers advantages for contextualized embeddings, such as
those generated by BERT, but only when trained on corpora of modest,
cognitively plausible sizes. Code and grounded embeddings for English are
available at https://github.com/Hazel1994/Visually_Grounded_Word_Embeddings_2
Evaluating the Representational Hub of Language and Vision Models
The multimodal models used in the emerging field at the intersection of
computational linguistics and computer vision implement the bottom-up
processing of the `Hub and Spoke' architecture proposed in cognitive science to
represent how the brain processes and combines multi-sensory inputs. In
particular, the Hub is implemented as a neural network encoder. We investigate
the effect on this encoder of various vision-and-language tasks proposed in the
literature: visual question answering, visual reference resolution, and
visually grounded dialogue. To measure the quality of the representations
learned by the encoder, we use two kinds of analyses. First, we evaluate the
encoder pre-trained on the different vision-and-language tasks on an existing
diagnostic task designed to assess multimodal semantic understanding. Second,
we carry out a battery of analyses aimed at studying how the encoder merges and
exploits the two modalities.Comment: Accepted to IWCS 201
Don't Blame Distributional Semantics if it can't do Entailment
Distributional semantics has had enormous empirical success in Computational
Linguistics and Cognitive Science in modeling various semantic phenomena, such
as semantic similarity, and distributional models are widely used in
state-of-the-art Natural Language Processing systems. However, the theoretical
status of distributional semantics within a broader theory of language and
cognition is still unclear: What does distributional semantics model? Can it
be, on its own, a fully adequate model of the meanings of linguistic
expressions? The standard answer is that distributional semantics is not fully
adequate in this regard, because it falls short on some of the central aspects
of formal semantic approaches: truth conditions, entailment, reference, and
certain aspects of compositionality. We argue that this standard answer rests
on a misconception: These aspects do not belong in a theory of expression
meaning, they are instead aspects of speaker meaning, i.e., communicative
intentions in a particular context. In a slogan: words do not refer, speakers
do. Clearing this up enables us to argue that distributional semantics on its
own is an adequate model of expression meaning. Our proposal sheds light on the
role of distributional semantics in a broader theory of language and cognition,
its relationship to formal semantics, and its place in computational models.Comment: To appear in Proceedings of the 13th International Conference on
Computational Semantics (IWCS 2019), Gothenburg, Swede
Context-aware Captions from Context-agnostic Supervision
We introduce an inference technique to produce discriminative context-aware
image captions (captions that describe differences between images or visual
concepts) using only generic context-agnostic training data (captions that
describe a concept or an image in isolation). For example, given images and
captions of "siamese cat" and "tiger cat", we generate language that describes
the "siamese cat" in a way that distinguishes it from "tiger cat". Our key
novelty is that we show how to do joint inference over a language model that is
context-agnostic and a listener which distinguishes closely-related concepts.
We first apply our technique to a justification task, namely to describe why an
image contains a particular fine-grained category as opposed to another
closely-related category of the CUB-200-2011 dataset. We then study
discriminative image captioning to generate language that uniquely refers to
one of two semantically-similar images in the COCO dataset. Evaluations with
discriminative ground truth for justification and human studies for
discriminative image captioning reveal that our approach outperforms baseline
generative and speaker-listener approaches for discrimination.Comment: Accepted to CVPR 2017 (Spotlight
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
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