Do you want to publish a course? Click here

A CCG-Based Version of the DisCoCat Framework

نسخة قائمة على مركبات الكربونات الكترونية من إطار التراجع

251   0   0   0.0 ( 0 )
 Publication date 2021
and research's language is English
 Created by Shamra Editor




Ask ChatGPT about the research

While the DisCoCat model (Coecke et al., 2010) has been proved a valuable tool for studying compositional aspects of language at the level of semantics, its strong dependency on pregroup grammars poses important restrictions: first, it prevents large-scale experimentation due to the absence of a pregroup parser; and second, it limits the expressibility of the model to context-free grammars. In this paper we solve these problems by reformulating DisCoCat as a passage from Combinatory Categorial Grammar (CCG) to a category of semantics. We start by showing that standard categorial grammars can be expressed as a biclosed category, where all rules emerge as currying/uncurrying the identity; we then proceed to model permutation-inducing rules by exploiting the symmetry of the compact closed category encoding the word meaning. We provide a proof of concept for our method, converting Alice in Wonderland'' into DisCoCat form, a corpus that we make available to the community.



References used
https://aclanthology.org/
rate research

Read More

Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject. We take a critic al look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks' syntactic ability. Our fine-grained analyses of results on the long-range French object-verb agreement show that contrary to LSTMs, Transformers are able to capture a non-trivial amount of grammatical structure.
Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings. Current methods mainly focus on learning embeddings for words while embeddings of linguistic information ( referred to as grain embeddings) are discarded after the learning. This work proposes a framework field embedding to jointly learn both word and grain embeddings by incorporating morphological, phonetic, and syntactical linguistic fields. The framework leverages an innovative fine-grained pipeline that integrates multiple linguistic fields and produces high-quality grain sequences for learning supreme word representations. A novel algorithm is also designed to learn embeddings for words and grains by capturing information that is contained within each field and that is shared across them. Experimental results of lexical tasks and downstream natural language processing tasks illustrate that our framework can learn better word embeddings and grain embeddings. Qualitative evaluations show grain embeddings effectively capture the semantic information.
This study seeks to clarify how Bernard Shaw in his play, Pygmalion, modifies and modernizes Charles Perrault‘s Cinderella. In this play, Shaw presents his heroine not only as a romantic heroine, but also as an emerging feminist unwilling to settl e for anything less than she deserves. Shaw modifies Charles Perrault‘s Cinderella or The Little Glass Slipper to reflect the current aspirations of feminism. The inclusion of mythic romantic elements throughout the play set the stage for a classic fairy tale ending denied by Eliza Doolittle‘s ideals and Henry Higgins‘s selfishness. Shaw presents the character of Eliza with feminist ideals and shows a realistic interpretation of what happened after Cinderella‘s transformation when she no longer wished to submit to the Prince.
Pimentel et al. (2020) recently analysed probing from an information-theoretic perspective. They argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactl y the same information about a target task as the original sentences. The mutual information, however, assumes the true probability distribution of a pair of random variables is known, leading to unintuitive results in settings where it is not. This paper proposes a new framework to measure what we term Bayesian mutual information, which analyses information from the perspective of Bayesian agents---allowing for more intuitive findings in scenarios with finite data. For instance, under Bayesian MI we have that data can add information, processing can help, and information can hurt, which makes it more intuitive for machine learning applications. Finally, we apply our framework to probing where we believe Bayesian mutual information naturally operationalises ease of extraction by explicitly limiting the available background knowledge to solve a task.
Driving simulators are valuable research tool for conducting driving studies instead of conducting these studies on the real roads. However, to be accepted as a representative of the real world, a driving simulator must provide an acceptable degree o f realism. It is always a goal of the designers of driving simulators to increase their degree of realism as possible. However, increasing the realism of a driving simulator leads to increasing its cost beyond the allocated cost. So it is common to build a driving simulator initially with an acceptable degree of realism and then have it undergo continuous changes to increase its realism whenever there is a chance to do so. The objective of this paper is to present a modular design of the software components of a fixed-base driving simulator. By following this design, it is possible to start building a simulator with a degree of realism that can continually be increased by improving each of the software components alone without the need to radically change other components of the simulator. This design also helps to build economical alternatives while conducting a study on a simulator and to collect the experiment data by providing specialized software components for these tasks.

suggested questions

comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا