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Appraisal Theories for Emotion Classification in Text

Apr 07, 2020
Jan Hofmann, Enrica Troiano, Kai Sassenberg, Roman Klinger

Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events (for instance, that somebody experiences fear when they discover a snake because of the appraisal as being an unpleasant and non-controllable situation), even without having access to explicit reports what an experiencer of an emotion is feeling (for instance expressing this with the words "I am afraid."). Automatic classification approaches therefore need to learn properties of events as latent variables (for instance that the uncertainty and effort associated with discovering the snake leads to fear). With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events and show their potential for emotion classification when being encoded in classification models. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories.


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Floods Detection in Twitter Text and Images

Nov 30, 2020
Naina Said, Kashif Ahmad, Asma Gul, Nasir Ahmad, Ala Al-Fuqaha

In this paper, we present our methods for the MediaEval 2020 Flood Related Multimedia task, which aims to analyze and combine textual and visual content from social media for the detection of real-world flooding events. The task mainly focuses on identifying floods related tweets relevant to a specific area. We propose several schemes to address the challenge. For text-based flood events detection, we use three different methods, relying on Bog of Words (BOW) and an Italian Version of Bert individually and in combination, achieving an F1-score of 0.77%, 0.68%, and 0.70% on the development set, respectively. For the visual analysis, we rely on features extracted via multiple state-of-the-art deep models pre-trained on ImageNet. The extracted features are then used to train multiple individual classifiers whose scores are then combined in a late fusion manner achieving an F1-score of 0.75%. For our mandatory multi-modal run, we combine the classification scores obtained with the best textual and visual schemes in a late fusion manner. Overall, better results are obtained with the multimodal scheme achieving an F1-score of 0.80% on the development set.

* 3 pages 

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On-Device Text Image Super Resolution

Nov 20, 2020
Dhruval Jain, Arun D Prabhu, Gopi Ramena, Manoj Goyal, Debi Prasanna Mohanty, Sukumar Moharana, Naresh Purre

Recent research on super-resolution (SR) has witnessed major developments with the advancements of deep convolutional neural networks. There is a need for information extraction from scenic text images or even document images on device, most of which are low-resolution (LR) images. Therefore, SR becomes an essential pre-processing step as Bicubic Upsampling, which is conventionally present in smartphones, performs poorly on LR images. To give the user more control over his privacy, and to reduce the carbon footprint by reducing the overhead of cloud computing and hours of GPU usage, executing SR models on the edge is a necessity in the recent times. There are various challenges in running and optimizing a model on resource-constrained platforms like smartphones. In this paper, we present a novel deep neural network that reconstructs sharper character edges and thus boosts OCR confidence. The proposed architecture not only achieves significant improvement in PSNR over bicubic upsampling on various benchmark datasets but also runs with an average inference time of 11.7 ms per image. We have outperformed state-of-the-art on the Text330 dataset. We also achieve an OCR accuracy of 75.89% on the ICDAR 2015 TextSR dataset, where ground truth has an accuracy of 78.10%.

* Accepted to the International Conference on Pattern Recognition(ICPR), 2020 

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Bilingual Text Extraction as Reading Comprehension

Apr 29, 2020
Katsuki Chousa, Masaaki Nagata, Masaaki Nishino

In this paper, we propose a method to extract bilingual texts automatically from noisy parallel corpora by framing the problem as a token-level span prediction, such as SQuAD-style Reading Comprehension. To extract a span of the target document that is a translation of a given source sentence (span), we use either QANet or multilingual BERT. QANet can be trained for a specific parallel corpus from scratch, while multilingual BERT can utilize pre-trained multilingual representations. For the span prediction method using QANet, we introduce a total optimization method using integer linear programming to achieve consistency in the predicted parallel spans. We conduct a parallel sentence extraction experiment using simulated noisy parallel corpora with two language pairs (En-Fr and En-Ja) and find that the proposed method using QANet achieves significantly better accuracy than a baseline method using two bi-directional RNN encoders, particularly for distant language pairs (En-Ja). We also conduct a sentence alignment experiment using En-Ja newspaper articles and find that the proposed method using multilingual BERT achieves significantly better accuracy than a baseline method using a bilingual dictionary and dynamic programming.

* 7 pages 

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Recognition of Implicit Geographic Movement in Text

Jan 30, 2022
Scott Pezanowski, Prasenjit Mitra

Analyzing the geographic movement of humans, animals, and other phenomena is a growing field of research. This research has benefited urban planning, logistics, animal migration understanding, and much more. Typically, the movement is captured as precise geographic coordinates and time stamps with Global Positioning Systems (GPS). Although some research uses computational techniques to take advantage of implicit movement in descriptions of route directions, hiking paths, and historical exploration routes, innovation would accelerate with a large and diverse corpus. We created a corpus of sentences labeled as describing geographic movement or not and including the type of entity moving. Creating this corpus proved difficult without any comparable corpora to start with, high human labeling costs, and since movement can at times be interpreted differently. To overcome these challenges, we developed an iterative process employing hand labeling, crowd voting for confirmation, and machine learning to predict more labels. By merging advances in word embeddings with traditional machine learning models and model ensembling, prediction accuracy is at an acceptable level to produce a large silver-standard corpus despite the small gold-standard corpus training set. Our corpus will likely benefit computational processing of geography in text and spatial cognition, in addition to detection of movement.

* Proceedings of The 12th Language Resources and Evaluation Conference, 2047-2056 (2020) 

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Uncertainty-Aware Reliable Text Classification

Jul 15, 2021
Yibo Hu, Latifur Khan

Deep neural networks have significantly contributed to the success in predictive accuracy for classification tasks. However, they tend to make over-confident predictions in real-world settings, where domain shifting and out-of-distribution (OOD) examples exist. Most research on uncertainty estimation focuses on computer vision because it provides visual validation on uncertainty quality. However, few have been presented in the natural language process domain. Unlike Bayesian methods that indirectly infer uncertainty through weight uncertainties, current evidential uncertainty-based methods explicitly model the uncertainty of class probabilities through subjective opinions. They further consider inherent uncertainty in data with different root causes, vacuity (i.e., uncertainty due to a lack of evidence) and dissonance (i.e., uncertainty due to conflicting evidence). In our paper, we firstly apply evidential uncertainty in OOD detection for text classification tasks. We propose an inexpensive framework that adopts both auxiliary outliers and pseudo off-manifold samples to train the model with prior knowledge of a certain class, which has high vacuity for OOD samples. Extensive empirical experiments demonstrate that our model based on evidential uncertainty outperforms other counterparts for detecting OOD examples. Our approach can be easily deployed to traditional recurrent neural networks and fine-tuned pre-trained transformers.

* KDD 2021 

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Low-Resource Speech-to-Text Translation

Jun 18, 2018
Sameer Bansal, Herman Kamper, Karen Livescu, Adam Lopez, Sharon Goldwater

Speech-to-text translation has many potential applications for low-resource languages, but the typical approach of cascading speech recognition with machine translation is often impossible, since the transcripts needed to train a speech recognizer are usually not available for low-resource languages. Recent work has found that neural encoder-decoder models can learn to directly translate foreign speech in high-resource scenarios, without the need for intermediate transcription. We investigate whether this approach also works in settings where both data and computation are limited. To make the approach efficient, we make several architectural changes, including a change from character-level to word-level decoding. We find that this choice yields crucial speed improvements that allow us to train with fewer computational resources, yet still performs well on frequent words. We explore models trained on between 20 and 160 hours of data, and find that although models trained on less data have considerably lower BLEU scores, they can still predict words with relatively high precision and recall---around 50% for a model trained on 50 hours of data, versus around 60% for the full 160 hour model. Thus, they may still be useful for some low-resource scenarios.

* Added references; results remain unchanged. Accepted to Interspeech 2018 

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DGST: a Dual-Generator Network for Text Style Transfer

Oct 27, 2020
Xiao Li, Guanyi Chen, Chenghua Lin, Ruizhe Li

We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer. Our model employs two generators only, and does not rely on any discriminators or parallel corpus for training. Both quantitative and qualitative experiments on the Yelp and IMDb datasets show that our model gives competitive performance compared to several strong baselines with more complicated architecture designs.

* Accepted by EMNLP 2020, camera ready version 

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Medical Text Classification using Convolutional Neural Networks

Apr 22, 2017
Mark Hughes, Irene Li, Spyros Kotoulas, Toyotaro Suzumura

We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information. Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.


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