Predicting emergent linguistic compositions through time: Syntactic
frame extension via multimodal chaining
- URL: http://arxiv.org/abs/2109.04652v1
- Date: Fri, 10 Sep 2021 03:42:07 GMT
- Title: Predicting emergent linguistic compositions through time: Syntactic
frame extension via multimodal chaining
- Authors: Lei Yu, Yang Xu
- Abstract summary: We develop a framework that exploits the cognitive mechanisms of chaining and multimodal knowledge to predict compositional expressions through time.
We present the syntactic frame extension model (SFEM) that draws on the theory of chaining and knowledge from "percept", "concept", and "language"
We show that multimodal SFEM predicts newly emerged verb syntax and arguments substantially better than competing models using purely linguistic or unimodal knowledge.
- Score: 8.254139827478355
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Natural language relies on a finite lexicon to express an unbounded set of
emerging ideas. One result of this tension is the formation of new
compositions, such that existing linguistic units can be combined with emerging
items into novel expressions. We develop a framework that exploits the
cognitive mechanisms of chaining and multimodal knowledge to predict emergent
compositional expressions through time. We present the syntactic frame
extension model (SFEM) that draws on the theory of chaining and knowledge from
"percept", "concept", and "language" to infer how verbs extend their frames to
form new compositions with existing and novel nouns. We evaluate SFEM
rigorously on the 1) modalities of knowledge and 2) categorization models of
chaining, in a syntactically parsed English corpus over the past 150 years. We
show that multimodal SFEM predicts newly emerged verb syntax and arguments
substantially better than competing models using purely linguistic or unimodal
knowledge. We find support for an exemplar view of chaining as opposed to a
prototype view and reveal how the joint approach of multimodal chaining may be
fundamental to the creation of literal and figurative language uses including
metaphor and metonymy.
Related papers
- The Problem of Alignment [1.2277343096128712]
Large Language Models produce sequences learned as statistical patterns from large corpora.
After initial training models must be aligned with human values, prefer certain continuations over others.
We examine this practice of structuration as a two-way interaction between users and models.
arXiv Detail & Related papers (2023-12-30T11:44:59Z) - Towards More Unified In-context Visual Understanding [74.55332581979292]
We present a new ICL framework for visual understanding with multi-modal output enabled.
First, we quantize and embed both text and visual prompt into a unified representational space.
Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them.
arXiv Detail & Related papers (2023-12-05T06:02:21Z) - From Word Models to World Models: Translating from Natural Language to
the Probabilistic Language of Thought [124.40905824051079]
We propose rational meaning construction, a computational framework for language-informed thinking.
We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought.
We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings.
We extend our framework to integrate cognitively-motivated symbolic modules.
arXiv Detail & Related papers (2023-06-22T05:14:00Z) - Geometry of Language [0.0]
We present a fresh perspective on language, combining ideas from various sources, but mixed in a new synthesis.
The question is whether we can formulate an elegant formalism, a universal grammar or a mechanism which explains significant aspects of the human faculty of language.
We describe such a mechanism, which differs from existing logical and grammatical approaches by its geometric nature.
arXiv Detail & Related papers (2023-03-09T12:22:28Z) - Variational Cross-Graph Reasoning and Adaptive Structured Semantics
Learning for Compositional Temporal Grounding [143.5927158318524]
Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence.
We introduce a new Compositional Temporal Grounding task and construct two new dataset splits.
We argue that the inherent structured semantics inside the videos and language is the crucial factor to achieve compositional generalization.
arXiv Detail & Related papers (2023-01-22T08:02:23Z) - Syntactic Persistence in Language Models: Priming as a Window into
Abstract Language Representations [0.38498574327875945]
We investigate the extent to which modern, neural language models are susceptible to syntactic priming.
We introduce a novel metric and release Prime-LM, a large corpus where we control for various linguistic factors which interact with priming strength.
We report surprisingly strong priming effects when priming with multiple sentences, each with different words and meaning but with identical syntactic structure.
arXiv Detail & Related papers (2021-09-30T10:38:38Z) - Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese
Pre-trained Language Models [62.41139712595334]
We propose a novel pre-training paradigm for Chinese -- Lattice-BERT.
We construct a lattice graph from the characters and words in a sentence and feed all these text units into transformers.
We show that our model can bring an average increase of 1.5% under the 12-layer setting.
arXiv Detail & Related papers (2021-04-15T02:36:49Z) - Decomposing lexical and compositional syntax and semantics with deep
language models [82.81964713263483]
The activations of language transformers like GPT2 have been shown to linearly map onto brain activity during speech comprehension.
Here, we propose a taxonomy to factorize the high-dimensional activations of language models into four classes: lexical, compositional, syntactic, and semantic representations.
The results highlight two findings. First, compositional representations recruit a more widespread cortical network than lexical ones, and encompass the bilateral temporal, parietal and prefrontal cortices.
arXiv Detail & Related papers (2021-03-02T10:24:05Z) - Multi-sense embeddings through a word sense disambiguation process [2.2344764434954256]
Most Suitable Sense.
(MSSA) disambiguates and annotates each word by its specific sense, considering the semantic effects of its context.
We test our approach on six different benchmarks for the word similarity task, showing that our approach can produce state-of-the-art results.
arXiv Detail & Related papers (2021-01-21T16:22:34Z) - Compositionality and Generalization in Emergent Languages [42.68870559695238]
We study whether the language emerging in deep multi-agent simulations possesses a similar ability to refer to novel primitive combinations.
We find no correlation between the degree of compositionality of an emergent language and its ability to generalize.
The more compositional a language is, the more easily it will be picked up by new learners.
arXiv Detail & Related papers (2020-04-20T08:30:14Z) - A Benchmark for Systematic Generalization in Grounded Language
Understanding [61.432407738682635]
Humans easily interpret expressions that describe unfamiliar situations composed from familiar parts.
Modern neural networks, by contrast, struggle to interpret novel compositions.
We introduce a new benchmark, gSCAN, for evaluating compositional generalization in situated language understanding.
arXiv Detail & Related papers (2020-03-11T08:40:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.