Sarcasm detection on Twitter

State-of-the-art approaches for sarcasm detection in social media combine lexical clues with contextual information surrounding the potentially sarcastic posting including author information. This article presents detailed methods for performing contextualizing sarcasm detection on Twitter, including data extraction, feature engineering and classification model settings. I reproduce the state-of-the-art results reported by Bamman and Smith (2015).Informatio

Self-, other-, and joint monitoring using forward models

In the psychology of language, most accounts of self-monitoring assume that it is based on comprehension. Here we outline and develop the alternative account proposed by Pickering and Garrod (2013), in which speakers construct forward models of their upcoming utterances and compare them with the utterance as they produce them. We propose that speakers compute inverse models derived from the discrepancy (error) between the utterance and the predicted utterance and use that to modify their production command or (occasionally) begin anew. We then propose that comprehenders monitor other people’s speech by simulating their utterances using covert imitation and forward models, and then comparing those forward models with what they hear. They use the discrepancy to compute inverse models and modify their representation of the speaker’s production command, or realize that their representation is incorrect and may develop a new production command. We then discuss monitoring in dialogue, paying attention to sequential contributions, concurrent feedback, and the relationship between monitoring and alignment

To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills

Communicating shapes our social word. For a robot to be considered social and being consequently integrated in our social environment it is fundamental to understand some of the dynamics that rule human-human communication. In this work, we tackle the problem of Addressee Estimation, the ability to understand an utterance's addressee, by interpreting and exploiting non-verbal bodily cues from the speaker. We do so by implementing an hybrid deep learning model composed of convolutional layers and LSTM cells taking as input images portraying the face of the speaker and 2D vectors of the speaker's body posture. Our implementation choices were guided by the aim to develop a model that could be deployed on social robots and be efficient in ecological scenarios. We demonstrate that our model is able to solve the Addressee Estimation problem in terms of addressee localisation in space, from a robot ego-centric point of view.Comment: Accepted version of a paper published at 2023 International Joint Conference on Neural Networks (IJCNN). Please find the published version and info to cite the paper at https://doi.org/10.1109/IJCNN54540.2023.10191452 . 10 pages, 8 Figures, 3 Table

Disentangling meaning in the brain: Left temporal involvement in agreement processing

Published online 18 November 2016Sentence comprehension is successfully accomplished by means of a form-to-meaning mapping procedure that relies on the extraction of morphosyntactic information from the input and its mapping to higher-level semantic–discourse representations. In this study, we sought to determine whether neuroanatomically distinct brain regions are involved in the processing of different types of information contained in the propositional meaning of a sentence, namely person and number. While person information indexes the role that an individual has in discourse (i.e., the speaker, the addressee or the entity being talked about by speaker and addressee), number indicates its cardinality (i.e., a single entity vs a multitude of entities). An event-related functional magnetic resonance imaging (fMRI) experiment was run using agreement-Correct and Person- and Number-violated sentences in Spanish, to disentangle the processing mechanisms and neural substrates associated with the building of discourse and cardinality representations. The contrast between Person and Number Violations showed qualitative and quantitative differences. A greater response for person compared to number was found in the left middle temporal gyrus (LMTG). However, critically, a posterior-to-anterior functional gradient emerged within this region. While the posterior portion of the LMTG was sensitive to both Person and Number Violations, the anterior portion of this region showed selective response for Person Violations. These results confirm that the comprehension of the propositional meaning of a sentence results from a composite, feature-sensitive mechanism of form-to-meaning mapping in which the nodes of the language network are differentially involved.BCBL acknowledges funding from Ayuda Centro de Excelencia Severo Ochoa SEV-2015-0490. S.M. acknowledges funding from the Gipuzkoako Foru Aldundia Fellowship Program and from grant PI_2014_38 from the Basque Government. N.M. was funded by grant PSI2012- 32350 and PSI2015-65694-P from the Spanish Ministry of Economy and Competitiveness. M.C was funded by grant PSI2012-31448 from the Spanish Ministry of Science and Innovation and ERC-2011-ADG-295362 from the European Research Council

Towards a complete multiple-mechanism account of predictive language processing [Commentary on Pickering & Garrod]

Although we agree with Pickering & Garrod (P&G) that prediction-by-simulation and prediction-by-association are important mechanisms of anticipatory language processing, this commentary suggests that they: (1) overlook other potential mechanisms that might underlie prediction in language processing, (2) overestimate the importance of prediction-by-association in early childhood, and (3) underestimate the complexity and significance of several factors that might mediate prediction during language processing

An integrated theory of language production and comprehension

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal