Inductive Bias and Language Expressivity in Emergent Communication
- URL: http://arxiv.org/abs/2012.02875v1
- Date: Fri, 4 Dec 2020 22:20:55 GMT
- Title: Inductive Bias and Language Expressivity in Emergent Communication
- Authors: Shangmin Guo, Yi Ren, Agnieszka S{\l}owik, Kory Mathewson
- Abstract summary: We investigate how the type of the language game affects the emergent language.
We show that languages emerged from different games have different compositionality and further different expressivity.
- Score: 6.043034177891378
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Referential games and reconstruction games are the most common game types for
studying emergent languages. We investigate how the type of the language game
affects the emergent language in terms of: i) language compositionality and ii)
transfer of an emergent language to a task different from its origin, which we
refer to as language expressivity. With empirical experiments on a handcrafted
symbolic dataset, we show that languages emerged from different games have
different compositionality and further different expressivity.
Related papers
- Analyzing The Language of Visual Tokens [48.62180485759458]
We take a natural-language-centric approach to analyzing discrete visual languages.
We show that higher token innovation drives greater entropy and lower compression, with tokens predominantly representing object parts.
We also show that visual languages lack cohesive grammatical structures, leading to higher perplexity and weaker hierarchical organization compared to natural languages.
arXiv Detail & Related papers (2024-11-07T18:59:28Z) - Multilingual Multi-Figurative Language Detection [14.799109368073548]
figurative language understanding is highly understudied in a multilingual setting.
We introduce multilingual multi-figurative language modelling, and provide a benchmark for sentence-level figurative language detection.
We develop a framework for figurative language detection based on template-based prompt learning.
arXiv Detail & Related papers (2023-05-31T18:52:41Z) - Multi-lingual and Multi-cultural Figurative Language Understanding [69.47641938200817]
Figurative language permeates human communication, but is relatively understudied in NLP.
We create a dataset for seven diverse languages associated with a variety of cultures: Hindi, Indonesian, Javanese, Kannada, Sundanese, Swahili and Yoruba.
Our dataset reveals that each language relies on cultural and regional concepts for figurative expressions, with the highest overlap between languages originating from the same region.
All languages exhibit a significant deficiency compared to English, with variations in performance reflecting the availability of pre-training and fine-tuning data.
arXiv Detail & Related papers (2023-05-25T15:30:31Z) - Transparency Helps Reveal When Language Models Learn Meaning [71.96920839263457]
Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations, both autoregressive and masked language models learn to emulate semantic relations between expressions.
Turning to natural language, our experiments with a specific phenomenon -- referential opacity -- add to the growing body of evidence that current language models do not well-represent natural language semantics.
arXiv Detail & Related papers (2022-10-14T02:35:19Z) - Analyzing Gender Representation in Multilingual Models [59.21915055702203]
We focus on the representation of gender distinctions as a practical case study.
We examine the extent to which the gender concept is encoded in shared subspaces across different languages.
arXiv Detail & Related papers (2022-04-20T00:13:01Z) - Discovering Representation Sprachbund For Multilingual Pre-Training [139.05668687865688]
We generate language representation from multilingual pre-trained models and conduct linguistic analysis.
We cluster all the target languages into multiple groups and name each group as a representation sprachbund.
Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.
arXiv Detail & Related papers (2021-09-01T09:32:06Z) - Expressivity of Emergent Language is a Trade-off between Contextual
Complexity and Unpredictability [7.765925231148388]
We propose a definition of partial order between expressivity based on the generalisation performance across different language games.
We also validate the hypothesis that expressivity of emergent languages is a trade-off between the complexity and unpredictability of the context.
We show that using our contrastive loss alleviates the collapse of message types seen using standard referential loss functions.
arXiv Detail & Related papers (2021-06-07T21:57:11Z) - Emergent Communication of Generalizations [13.14792537601313]
We argue that communicating about a single object in a shared visual context is prone to overfitting and does not encourage language useful beyond concrete reference.
We propose games that require communicating generalizations over sets of objects representing abstract visual concepts.
We find that these games greatly improve systematicity and interpretability of the learned languages.
arXiv Detail & Related papers (2021-06-04T19:02:18Z) - Rediscovering the Slavic Continuum in Representations Emerging from
Neural Models of Spoken Language Identification [16.369477141866405]
We present a neural model for Slavic language identification in speech signals.
We analyze its emergent representations to investigate whether they reflect objective measures of language relatedness.
arXiv Detail & Related papers (2020-10-22T18:18:19Z) - Bridging Linguistic Typology and Multilingual Machine Translation with
Multi-View Language Representations [83.27475281544868]
We use singular vector canonical correlation analysis to study what kind of information is induced from each source.
We observe that our representations embed typology and strengthen correlations with language relationships.
We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
arXiv Detail & Related papers (2020-04-30T16:25:39Z)
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.