LT3 at SemEval-2020 Task 9 : cross-lingual embeddings for sentiment analysis of Hinglish social media text

This paper describes our contribution to the SemEval-2020 Task 9 on Sentiment Analysis for Code-mixed Social Media Text. We investigated two approaches to solve the task of Hinglish sentiment analysis. The first approach uses cross-lingual embeddings resulting from projecting Hinglish and pre-trained English FastText word embeddings in the same space. The second approach incorporates pre-trained English embeddings that are incrementally retrained with a set of Hinglish tweets. The results show that the second approach performs best, with an F1-score of 70.52% on the held-out test data

LT3 at SemEval-2020 Task 8 : multi-modal multi-task learning for memotion analysis

Internet memes have become a very popular mode of expression on social media networks today. Their multi-modal nature, caused by a mixture of text and image, makes them a very challenging research object for automatic analysis. In this paper, we describe our contribution to the SemEval2020 Memotion Analysis Task. We propose a Multi-Modal Multi-Task learning system, which incorporates “memebeddings”, viz. joint text and vision features, to learn and optimize for all three Memotion subtasks simultaneously. The experimental results show that the proposed system constantly outperforms the competition’s baseline, and the system setup with continual learning (where tasks are trained sequentially) obtains the best classification F1-scores

Does BERT understand sentiment? Leveraging comparisons between contextual and non-contextual embeddings to improve aspect-based sentiment models

When performing Polarity Detection for different words in a sentence, we need to look at the words around to understand the sentiment. Massively pretrained language models like BERT can encode not only just the words in a document but also the context around the words along with them. This begs the questions, "Does a pretrain language model also automatically encode sentiment information about each word?" and "Can it be used to infer polarity towards different aspects?". In this work we try to answer this question by showing that training a comparison of a contextual embedding from BERT and a generic word embedding can be used to infer sentiment. We also show that if we finetune a subset of weights the model built on comparison of BERT and generic word embedding, it can get state of the art results for Polarity Detection in Aspect Based Sentiment Classification datasets

Literary criticism 2.0 : a digital analysis of the professional and community-driven evaluative talk of literature surrounding the Ingeborg Bachmann Prize

In recent times, the knowledge of a limited number of professional literary critics has been challenged by technological developments and the “wisdom of the crowds”. Ample research has been devoted to shifts in traditional gatekeepers, such as hybrid publishers (Vandersmissen 2020) and prizes (English 2009, Sapiro 2016), and to the demise of professional critics’ authority at the hands of online literary criticism (Dorleijn et al. 2009, Löffler 2017, Schneider 2018; Kempke et al. 2019, Chong 2020). Nevertheless, comparatively little research (Allington 2016, Kellermann et al. 2016; Kellermann and Mehling 2017; Bogaert 2017, Pianzola et al. 2020) has actually attempted to directly ingest and mine the content of user-generated online literary criticism, as well to examine and the role of peer-to-peer recommendation systems and layperson critics as new literary gatekeepers and cultural transmitters. This project aims to study the differences between professional critics and this ‘wisdom of the crowd’, especially since traditional gatekeepers of the literary field (publishers, reviewers) are increasingly trying to tap the potential of online reading communities. We will present the preliminary results of the FWO-funded research project “Evaluation of literature by professional and layperson critics…

Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models

When performing Polarity Detection for different words in a sentence, we need to look at the words around to understand the sentiment. Massively pretrained language models like BERT can encode not only just the words in a document but also the context around the words along with them. This begs the questions, "Does a pretrain language model also automatically encode sentiment information about each word?" and "Can it be used to infer polarity towards different aspects?". In this work we try to answer this question by showing that training a comparison of a contextual embedding from BERT and a generic word embedding can be used to infer sentiment. We also show that if we finetune a subset of weights the model built on comparison of BERT and generic word embedding, it can get state of the art results for Polarity Detection in Aspect Based Sentiment Classification datasets

Multi-domain document layout understanding using few-shot object detection

We try to address the problem of document layout understanding using a simple algorithm which generalizes across multiple domains while training on just few examples per domain. We approach this problem via supervised object detection method and propose a methodology to overcome the requirement of large datasets. We use the concept of transfer learning by pre-training our object detector on a simple artificial (source) dataset and fine-tuning it on a tiny domain specific (target) dataset. We show that this methodology works for multiple domains with training samples as less as 10 documents. We demonstrate the effect of each component of the methodology in the end result and show the superiority of this methodology over simple object detectors. We will open-source the code, trained models, source and target datasets upon acceptance

Aspect-based Sentiment Analysis for German: Analyzing Talk of Literature" Surrounding Literary Prizes on Social Media

Since the rise of social media, the authority of traditional professional literary critics has beensupplemented – or undermined, depending on the point of view – by technological developmentsand the emergence of community-driven online layperson literary criticism. So far, relatively littleresearch (Allington 2016, Kellermann et al. 2016, Kellermann and Mehling 2017, Bogaert 2017, Pi-anzola et al. 2020) has examined this layperson user-generated evaluative “talk of literature”instead of addressing traditional forms of consecration. In this paper, we examine the layper-son literary criticism pertaining to a prominent German-language literary award: the Ingeborg-Bachmann-Preis, awarded during the Tage der deutschsprachigen Literatur (TDDL).We propose an aspect-based sentiment analysis (ABSA) approach to discern the evaluativecriteria used to differentiate between ‘good’ and ‘bad’ literature. To this end, we collected a cor-pus of German social media reviews, retrieved from Twitter, and enriched it with manual ABSAannotations:aspectsand aspect categories (e.g. the motifs or themes in a text, the jury discus-sions and evaluations, ...),sentiment expressionsandnamed entities. In a next step, the manualannotations are used as training data for our ABSA pipeline including 1) aspect term categoryprediction and 2) aspect term polarity classification. Each pipeline component is developed usingstate-of-the-art pre-trained BERT models.Two sets of experiments were conducted for the aspect polarity detection: one where only theaspect embeddings were used and another where an additional context window of five adjoiningwords in either direction of the aspect was considered. We present the classification results forthe aspect category and aspect sentiment prediction subtasks for the Twitter corpus. Thesepreliminary experimental results show a good performance for the aspect category classification,with a macro and a weighted F1-score of 69% and 83% for the coarse-grained and 54% and 73% forthe fine-grained task, as well as for the aspect sentiment classification subtask, using an additionalcontext window, with a macro and a weighted F1-score of 70% and 71%, respectivel