Papers with computational model
PISA: A measure of Preference In Selection of Arguments to model verb argument recoverability (2020.starsem-1)
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| Challenge: | a computational model of the semantic recoverability of verb arguments is tested on direct objects and Instruments. |
| Approach: | They propose a computational model of the semantic recoverability of verb arguments . they use a selectional preference model to compute selectional preferences of verbs . |
| Outcome: | The proposed model can predict the recoverability of objects and Instruments at a much cheaper computational cost. |
A Computational Model of Latvian Morphology (2024.lrec-main)
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| Challenge: | a computational model of Latvian morphology provides a formal structure for Latvian word form inflection . the model explicitly enumerates and handles the many exceptions to the general Latvian inflation principles . |
| Approach: | They propose a computational model of Latvian morphology that provides a formal structure for Latvian word form inflection. |
| Outcome: | The proposed model provides a good coverage for modern Latvian literary language and potential to extend to Latgalian language. |
Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models (N18-2)
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| Challenge: | German particle verbs are complex verb structures that combine a prefix particle with a base verb. |
| Approach: | They propose a computational model to detect and distinguish analogies in meaning shifts between German base and complex verbs using a standard similarity model. |
| Outcome: | The proposed model detects and distinguishes analogies in meaning shifts between German base and complex verbs using a standard similarity model. |
From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory (2022.findings-naacl)
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| Challenge: | Fuzzy trace theory explains human risky decision-making by incorporating gists, i.e. fuzzy representations of information which capture only its quintessential meaning. |
| Approach: | They propose a computational framework which combines the effects of the underlying semantics and sentiments on text-based decision-making. |
| Outcome: | The proposed framework can be optimised to predict risky decision-making in groups and individuals. |
Truth-Conditional Captions for Time Series Data (2021.emnlp-main)
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| Challenge: | Existing models with attention mechanisms can generate fluent descriptions of salient patterns in time series, but they often generate factually incorrect descriptions. |
| Approach: | They propose a model which first runs small learned programs on the input time series, then identifies the programs/patterns which hold true for the given input, and finally conditions on *only* the chosen valid program to generate the output text description. |
| Outcome: | The proposed model extracts high-level patterns from the data and generates high precision captions even though it is built on a small space of modules. |
The neural dynamics of word recognition and integration (2023.emnlp-main)
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| Challenge: | Using a computational model of word recognition, listeners combine expectations about upcoming content with incremental sensory evidence. |
| Approach: | They fit this model to scalp EEG signals recorded as subjects passively listened to a fictional story and found that words require more than 150 ms of input to be recognized. |
| Outcome: | The proposed model formalizes this perceptual process in Bayesian decision theory and reveals distinct neural processing of words depending on whether or not they can be quickly recognized. |
What Can We Learn from Noun Substitutions in Revision Histories? (2020.coling-main)
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| Challenge: | Recent work shows that resulting improvements can be modelled computationally, assuming that each revision contributes to the improvement. |
| Approach: | They propose to model improvements in sentences using wikiHow revision histories by assuming that each revision contributes to the improvement. |
| Outcome: | The proposed model fails in cases where humans can resort to factual knowledge or intuitions about the required level of specificity. |
Contextual Argument Component Classification for Class Discussions (2020.coling-main)
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| Challenge: | Argument mining systems often consider contextual information when training to perform tasks such as argument component identification, classification, and relation extraction. |
| Approach: | They propose to incorporate speaker context and local discourse context into a model for classifying argument components in multi-party classroom discussions. |
| Outcome: | The proposed model improves when varying context size and position . the results support the claim that context size is important . |
Neural Activation Semantic Models: Computational lexical semantic models of localized neural activations (C18-1)
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| Challenge: | Neural activation models have been proposed to map word semantics to localized neural activations. |
| Approach: | They propose a computational model that estimates semantic similarity in the neural activation space and investigate its performance for various natural language processing tasks. |
| Outcome: | The proposed model performs better than state-of-the-art word embeddings for the task of semantic similarity estimation between very similar or very dissimilar words while performing well on other tasks such as entailment and word categorization. |
Computational Modeling of Affixoid Behavior in Chinese Morphology (2020.coling-main)
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| Challenge: | affixoid behavior in Mandarin Chinese is unclear due to polysemy and diachronic dynamics. |
| Approach: | They propose to use three quantitative features to model affixoid behavior in Mandarin Chinese to determine its status. |
| Outcome: | The proposed model shows that there are no clear criteria that can be used to identify an affix’s status in an isolating language like Mandarin Chinese. |
AMPERSAND: Argument Mining for PERSuAsive oNline Discussions (D19-1)
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| Challenge: | Argument mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text. |
| Approach: | They propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level models of argumentation. |
| Outcome: | The proposed model improves on existing models using pointer networks and a pre-trained language model. |
A Computational Architecture for the Morphology of Upper Tanana (L18-1)
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| Challenge: | a computational model of Upper Tanana is described to model the Dene language . the model uses lexical-inflectional verb classes to predict possible derivations and their morphological behavior. |
| Approach: | They propose a computational model of Upper Tanana, a highly endangered Dene language . the model parses and generates inflected Upper Tanans and uses a lexical-inflectional verb system to predict possible derivations and their morphological behavior. |
| Outcome: | The proposed model parses and generates inflected Upper Tanana verb forms . it also uses the language's verb theme category system to predict possible derivations and their morphological behavior . |
Surprisal Predicts Code-Switching in Chinese-English Bilingual Text (2020.emnlp-main)
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| Challenge: | a new study examines the propensity of bilinguals to switch languages . word surprisal and word entropy are important predictors of code-switching . |
| Approach: | They propose high cognitive effort as a reason for code-switching . they use a computational model of surprisal and word entropy to model code-changing . |
| Outcome: | The proposed model shows that word surprisal, but not entropy, is a significant predictor . sentence length is also a predictor, which has been related to sentence complexity . |
Modeling Northern Haida Verb Morphology (L18-1)
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| Challenge: | a computational model of the verbal morphology of Northern Haida is being developed . the model is capable of handling complex affixation patterns and morphophonological alternations . |
| Approach: | They propose a computational model of the verbal morphology of Northern Haida based on finite state machines with a focus on verbs. |
| Outcome: | The proposed model can handle complex affixation patterns and morphophonological alternations in the native language. |
Uncovering Probabilistic Implications in Typological Knowledge Bases (P19-1)
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| Challenge: | linguistic typology is concerned with mapping out the relationships between languages with structural and functional properties. |
| Approach: | They propose a computational model which identifies known and new linguistic universals and uncovers them worthy of further linguistic investigation. |
| Outcome: | The proposed model outperforms baselines and knowledge base baselines. |
Interpretable Emoji Prediction via Label-Wise Attention LSTMs (D18-1)
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| Challenge: | Emojis are the evolution of characterbased emoticons and are used to express ideas about a myriad of topics. |
| Approach: | They propose a label-wise attention mechanism to better understand emoji prediction . they propose to model e-mails with eojis and then label them based on their meaning . |
| Outcome: | The proposed model improves over baselines and does particularly well when predicting infrequent emojis. |
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis (2022.coling-1)
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| Challenge: | Aspect-term sentiment analysis (ATSA) is a fine-grained task that aims to infer the sentiment towards the given aspect-terms. |
| Approach: | They propose a novel ATSA method that is interpretable and has high accuracy . they propose SILTN, which is a neurosymbolic formalism, to improve the accuracy based on syntax knowledge distillation. |
| Outcome: | The proposed method is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language. |
Metaphorical Expressions in Automatic Arabic Sentiment Analysis (2020.lrec-1)
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| Challenge: | Existing algorithms and tools for sentiment analysis are lacking in dealing with Arabic metaphorical expressions. |
| Approach: | They propose to use Arabic metaphors in automatic Arabic sentiment analysis to examine the performance of a state-of-art Arabic sentiment tool on metaphors. |
| Outcome: | The proposed model outperforms the state-of-the-art sentiment analysis tool on metaphors and gain a deeper insight into the issue. |
Informal Persian Universal Dependency Treebank (2022.lrec-1)
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| Challenge: | phonological, morphological, and syntactic distinctions between formal and informal Persian are important . formal Persian is not a universally recognized form of language, but is a dialect of informal Persian . |
| Approach: | They develop an open-source treebank for informal Persian to train dependency parsers . they then train dependency lexicographers on existing treebanks and evaluate them on out-of-domain data . |
| Outcome: | The proposed treebanks show that they perform poorly when training on formal and informal Persians. |
Run Like a Girl! Sport-Related Gender Bias in Language and Vision (2023.findings-acl)
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| Challenge: | Existing social biases and stereotypes against certain groups are reproduced by computational models. |
| Approach: | They analyze gender bias in two Language and Vision datasets to find that they underrepresent women . they hypothesize that a bias affects human naming choices for people playing sports . |
| Outcome: | The proposed model reproduces gender bias in two Language and Vision datasets. |
From perception to production: how acoustic invariance facilitates articulatory learning in a self-supervised vocal imitation model (2025.emnlp-main)
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| Challenge: | Existing models that map variable acoustic inputs into appropriate articulatory movements without explicit instruction are inadequate for infants. |
| Approach: | They propose a model that maps acoustic inputs into articulatory movements without explicit instruction for infants. |
| Outcome: | The proposed model outperforms MFCC features in both single- and multi-speaker settings and provides optimal representations for articulatory learning. |
Which Sense Dominates Multisensory Semantic Understanding? A Brain Decoding Study (2024.lrec-main)
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| Challenge: | Decoding semantic meanings from brain activity is open to multisensory stimulation, as word meanings can be delivered by both auditory and visual inputs. |
| Approach: | They aim to develop a computational model to probing what information from the act of language understanding is represented in human brain. |
| Outcome: | The proposed model dissociates multisensory integration of word understanding into written text, spoken text and image perception respectively, exploring the decoding efficiency and reliability of unisensory information in the brain representation. |