Relation classification (sometimes called 'extraction') requires trustworthy datasets for fine-tuning large language models, as well as for evaluation.
Online medical forums have become a predominant platform for answering health-related information needs of consumers.
Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering.
For the regression task, VADEC, when trained with SenWave, achieves 7. 6% and 16. 5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively.
We design a novel convex optimization-based multi-criteria online subset selection algorithm that uses a thresholded concave function of selection variables.
The networked opinion diffusion in online social networks (OSN) is often governed by the two genres of opinions - endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news, feeds etc.
Training vision-based Autonomous driving models is a challenging problem with enormous practical implications.
A consumer-dependent (business-to-consumer) organization tends to present itself as possessing a set of human qualities, which is termed as the brand personality of the company.
We show that the welfare and fairness objectives can be in conflict with each other, and there is a need to maintain a balance between these objective while caring for them simultaneously.
DE-VAE achieves better control of sentiment as an attribute while preserving the content by learning a suitable lossless transformation network from the disentangled sentiment space to the desired entangled representation.
Manually extracting relevant aspects and opinions from large volumes of user-generated text is a time-consuming process.
We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other.
Travel time estimation is a fundamental problem in transportation science with extensive literature.
In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels.
Specifically, we experiment with fusing embeddings obtained from knowledge graph with the state-of-the-art approaches for NLI task (ESIM model).
Code-switching, the interleaving of two or more languages within a sentence or discourse is pervasive in multilingual societies.
Consequently, the best monolingual methods perform relatively poorly on code-switched text.
The recently released FEVER dataset provided benchmark results on a fact-checking task in which given a factual claim, the system must extract textual evidence (sets of sentences from Wikipedia pages) that support or refute the claim.
Moreover, in contrast with the state of the art, our decoder is able to provide the spatial coordinates of the atoms of the molecules it generates.
This paper proposes a graphical framework to extract opinionated sentences which highlight different contexts within a given news article by introducing the concept of diversity in a graphical model for opinion detection. We conduct extensive evaluations and find that the proposed modification leads to impressive improvement in performance and makes the final results of the model much more usable.
The use of microblogging platforms such as Twitter during crises has become widespread.
Code-Switching (CS) between two languages is extremely common in communities with societal multilingualism where speakers switch between two or more languages when interacting with each other.
Apart from the new predictor, another contribution is a rigorous protocol for benchmarking and reporting LP algorithms, which reveals the regions of strengths and weaknesses of all the predictors studied here, and establishes the new proposal as the most robust.