scholarly journals Incrementality and efficiency shape pragmatics across languages

2020 ◽  
Vol 117 (24) ◽  
pp. 13399-13404 ◽  
Author(s):  
Paula Rubio-Fernandez ◽  
Julian Jara-Ettinger
Keyword(s):  
Eye Tracking ◽  
Language Processing ◽  
Context Information ◽  
Visual Context ◽  
Human Communication ◽  
Pragmatic Information ◽  
Incoming Information ◽  
Speaker Intentions ◽  
Pragmatic Reasoning

To correctly interpret a message, people must attend to the context in which it was produced. Here we investigate how this process, known as pragmatic reasoning, is guided by two universal forces in human communication: incrementality and efficiency, with speakers of all languages interpreting language incrementally and making the most efficient use of the incoming information. Crucially, however, the interplay between these two forces results in speakers of different languages having different pragmatic information available at each point in processing, including inferences about speaker intentions. In particular, the position of adjectives relative to nouns (e.g., “black lamp” vs. “lamp black”) makes visual context information available in reverse orders. In an eye-tracking study comparing four unrelated languages that have been understudied with regard to language processing (Catalan, Hindi, Hungarian, and Wolof), we show that speakers of languages with an adjective–noun order integrate context by first identifying properties (e.g., color, material, or size), whereas speakers of languages with a noun–adjective order integrate context by first identifying kinds (e.g., lamps or chairs). Most notably, this difference allows listeners of adjective–noun descriptions to infer the speaker’s intention when using an adjective (e.g., “the black…” as implying “not the blue one”) and anticipate the target referent, whereas listeners of noun–adjective descriptions are subject to temporary ambiguity when deriving the same interpretation. We conclude that incrementality and efficiency guide pragmatic reasoning across languages, with different word orders having different pragmatic affordances.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

The integration of visual context information in facial emotion recognition in 5- to 15-year-olds

2016 ◽  
Vol 150 ◽  
pp. 252-271 ◽  
Author(s):  
Anne Theurel ◽  
Arnaud Witt ◽  
Jennifer Malsert ◽  
Fleur Lejeune ◽  
Chiara Fiorentini ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Context Information ◽  
Visual Context

scholarly journals A Survey on Using Gaze Behaviour for Natural Language Processing

2020 ◽  
Author(s):  
Sandeep Mathias ◽  
Diptesh Kanojia ◽  
Abhijit Mishra ◽  
Pushpak Bhattacharya
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Eye Tracking ◽  
Language Processing ◽  
Word Identification ◽  
Test Time ◽  
Gaze Behaviour ◽  
Cognitive Information ◽  
Essay Grading ◽  
Multiple Languages

Gaze behaviour has been used as a way to gather cognitive information for a number of years. In this paper, we discuss the use of gaze behaviour in solving different tasks in natural language processing (NLP) without having to record it at test time. This is because the collection of gaze behaviour is a costly task, both in terms of time and money. Hence, in this paper, we focus on research done to alleviate the need for recording gaze behaviour at run time. We also mention different eye tracking corpora in multiple languages, which are currently available and can be used in natural language processing. We conclude our paper by discussing applications in a domain - education - and how learning gaze behaviour can help in solving the tasks of complex word identification and automatic essay grading.

scholarly journals The male bias of a masculine generic pronoun: Evidence from eye-tracking and sentence evaluation

2020 ◽  
Author(s):  
Theresa Redl ◽  
Stefan L. Frank ◽  
Peter de Swart ◽  
Helen de Hoop
Keyword(s):  
Eye Tracking ◽  
Language Processing ◽  
Possessive Pronoun ◽  
Male Bias ◽  
Gender Information

Two experiments tested whether the Dutch possessive pronoun zijn ‘his’ gives rise to a gender inference and thus causes a male bias when used generically in sentences such as Everyone was putting on his shoes. Experiment 1 (N = 120, 48 male) was a conceptual replication of a previous eye-tracking study that had not found evidence of a male bias. The results of the current eye-tracking experiment showed the masculine generic pronoun to trigger a gender inference and cause a male bias, but for male participants and in neutral stereotype contexts only. No evidence for a male bias was thus found in stereotypically female and male contexts and for female participants altogether. Experiment 2 (N = 80, 40 male) used the same stimuli as Experiment 1, but employed the sentence evaluation paradigm. No evidence of a male bias was found in Experiment 2. Taken together, the results suggest that the masculine generic pronoun zijn ‘his’ can cause a male bias for male participants when no other gender information is provided, but only surfaces with a method such as eye-tracking, which taps directly into automatic language processing. Furthermore, the results suggest that the intended generic reading of the masculine possessive pronoun zijn ‘his’ is readily available for women.

scholarly journals Beyond robotic speech: mutual benefits to cognitive psychology and artificial intelligence from the study of multimodal communication

2020 ◽  
Author(s):  
Beata Grzyb ◽  
Gabriella Vigliocco
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Psychology ◽  
Language Learning ◽  
Language Processing ◽  
Computational Models ◽  
Eye Gaze ◽  
Human Communication ◽  
Embodied Agents ◽  
Human Machine Interaction ◽  
Neural Underpinnings

Language has predominately been studied as a unimodal phenomenon - as speech or text without much consideration of its physical and social context – this is true both in cognitive psychology/psycholinguistics as well as in artificial intelligence. However, in everyday life, language is most often used in face-to-face communication and in addition to structured speech it comprises a dynamic system of multiplex components such as gestures, eye gaze, mouth movements and prosodic modulation. Recently, cognitive scientists have started to realise the potential importance of multimodality for the understanding of human communication and its neural underpinnings; while AI scientists have begun to address how to integrate multimodality in order to improve communication between human and artificial embodied agent. We review here the existing literature on multimodal language learning and processing in humans and the literature on perception of artificial agents, their comprehension and production of multimodal cues and we discuss their main limitations. We conclude by arguing that by joining forces AI scientists can improve the effectiveness of human-machine interaction and increase the human-likeness and acceptance of embodied agents in society. In turn, computational models that generate language in artificial embodied agents constitute a unique research tool to investigate the underlying mechanisms that govern language processing and learning in humans.

"Units of Meaning" in Medical Documents: A Natural Language Processing Perspective

2014 ◽  
Vol 08 (03) ◽  
pp. 249-255
Author(s):  
Joseph R. Barr ◽  
Dimitri Popolov
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Free Form ◽  
Human Communication ◽  
Departure Point ◽  
Complicated System ◽  
Laboratory Results ◽  
Medical Documents

This paper discusses principles for the design of natural language processing (NLP) systems to automatically extract data from doctor's notes, laboratory results and other medical documents in free-form text. We argue that rather than searching for "atom units of meaning" in the text and then trying to generalize them into a broader set of documents through increasingly complicated system of rules, an NLP practitioner should take concepts as a whole and as a meaningful unit of text. This simplifies the rules and makes NLP system easier to maintain and adapt. The departure point is purely practical; however, a deeper investigation of typical problems with the implementation of such systems leads us to a discussion of broader linguistic theories underlying the NLP practices, such as metaphors theories and models of human communication.

A Deep Learning based Approach to Argument Recommendation

2021 ◽  
Author(s):  
Guangjie Li ◽  
Yi Tang ◽  
Biyi Yi ◽  
Xiang Zhang ◽  
Yan He
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Context Information ◽  
Data Embedding ◽  
Software Developers ◽  
Code Completion ◽  
Software Engineers ◽  
Large Corpus

Code completion is one of the most useful features provided by advanced IDEs and is widely used by software developers. However, as a kind of code completion, recommending arguments for method calls is less used. Most of existing argument recommendation approaches provide a long list of syntactically correct candidate arguments, which is difficult for software engineers to select the correct arguments from the long list. To this end, we propose a deep learning based approach to recommending arguments instantly when programmers type in method names they intend to invoke. First, we extract context information from a large corpus of opensource applications. Second, we preprocess the extracted dataset, which involves natural language processing and data embedding. Third, we feed the preprocessed dataset to a specially designed convolutional neural network to rank and recommend actual arguments. With the resulting CNN model trained with sample applications, we can sort the candidate arguments in a reasonable order and recommend the first one as the correct argument. We evaluate the proposed approach on 100 open-source Java applications. Results suggest that the proposed approach outperforms the state-of-theart approaches in recommending arguments.

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