Thailand

Understanding the gaps between job requirements and university curricula is crucial for improving student success and institutional effectiveness in higher education. In this context, natural language processing (NLP) can be leveraged to generate granular insights into where the gaps are and how they change. This paper proposes a three-dimensional research framework that combines NLP techniques with economic and educational research to quantify the alignment between course syllabi and job postings. We elaborate on key technical details of the framework and further discuss its potential positive impacts on practice, including unveiling the inequalities in and long-term consequences of education-occupation alignment to inform policymakers, and fostering information systems to support students, institutions and employers in the school-to-work pipeline.

IJCNLP-AACL 2021

A Research Framework for Understanding Education-Occupation Alignment with NLP Techniques

labor economics

semantic textual similarity

natural language processing

deep learning

text classification

education

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

workshop paper

It has become a common pattern in our field: One group introduces a language task, exemplified by a dataset, which they argue is challenging enough to serve as a benchmark. They also provide a baseline model for it, which then soon is improved upon by other groups. Often, research efforts then move on, and the pattern repeats itself. What is typically left implicit is the argumentation for why this constitutes progress, and progress towards what. In this paper, we try to step back for a moment from this pattern and work out possible argumentations and their parts.

Targeting the Benchmark: On Methodology in Current Natural Language Processing Research

Leaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models). Rather than replace leaderboards, we advocate a re-imagining so that they better highlight if and where progress is made. Building on educational testing, we create a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. Using this model, we analyze the ranking reliability of leaderboards. Afterwards, we show the model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples. We conclude with recommendations for future benchmark tasks.

Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?

Manual fact-checking does not scale well to serve the needs of the internet. This issue is further compounded in non-English contexts. In this paper, we discuss claim matching as a possible solution to scale fact-checking. We define claim matching as the task of identifying pairs of textual messages containing claims that can be served with one fact-check. We construct a novel dataset of WhatsApp tipline and public group messages alongside fact-checked claims that are first annotated for containing "claim-like statements" and then matched with potentially similar items and annotated for claim matching. Our dataset contains content in high-resource (English, Hindi) and lower-resource (Bengali, Malayalam, Tamil) languages. We train our own embedding model using knowledge distillation and a high-quality "teacher" model in order to address the imbalance in embedding quality between the low- and high-resource languages in our dataset. We provide evaluations on the performance of our solution and compare with baselines and existing state-of-the-art multilingual embedding models, namely LASER and LaBSE. We demonstrate that our performance exceeds LASER and LaBSE in all settings. We release our annotated datasets, codebooks, and trained embedding model to allow for further research.

Claim Matching Beyond English to Scale Global Fact-Checking

In this work, we introduce \datasetName: the largest publicly available multilingual dataset for factual verification of naturally existing real-world claims. The dataset contains short statements in 25 languages and is labeled for veracity by expert fact-checkers. The dataset includes a multilingual evaluation benchmark that measures both out-of-domain generalization, and zero-shot capabilities of the multilingual models. Using state-of-the-art multilingual transformer-based models, we develop several automated fact-checking models that, along with textual claims, make use of additional metadata and evidence from news stories retrieved using a search engine. Empirically, our best model attains an F-score of around 40%, suggesting that our dataset is a challenging benchmark for the evaluation of multilingual fact-checking models.

X-Fact: A New Benchmark Dataset for Multilingual Fact Checking

In this paper, we present Tintful, an NLP annotation software that can be used both to manually annotate texts and to fix mistakes in NLP pipelines, such as Stanford CoreNLP. Using a paradigm similar to wiki-like systems, a user who notices some wrong annotation can easily fix it and submit the resulting (and right) entry back to the tool developers. Moreover, Tintful can be used to easily annotate data from scratch. The input documents do not need to be in a particular format: starting from the plain text, the sentences are first annotated with CoreNLP, then the user can edit the annotations and submit everything back through a user-friendly interface.

Erase and Rewind: Manual Correction of NLP Output through a Web Interface

We introduce Explainable Scientific Research Assistant (ESRA), a literature discovery platform that augments search results with relevant details and explanations, aiding users in understanding more about their queries and the returned papers beyond existing literature search systems. Enabled by a knowledge graph we extracted from abstracts of 23k papers on the arXiv's cs.CL category, ESRA provides three main features: explanation (for why a paper is returned to the user), list of facts (that are relevant to the query), and graph visualization (drawing connections between the query and each paper with surrounding related entities). The experimental results with humans involved show that ESRA can accelerate the users' search process with paper explanations and helps them better explore the landscape of the topics of interest by exploiting the underlying knowledge graph. We provide the ESRA web application at http://esra.cp.eng.chula.ac.th/.

{ESRA}: Explainable Scientific Research Assistant

An essential operation in web corpus construction consists in retaining the desired content while discarding the rest. Another challenge finding one's way through websites. This article introduces a text discovery and extraction tool published under open-source license. Its installation and use is straightforward, notably from Python and on the command-line. The software allows for main text, comments and metadata extraction, while also providing building blocks for web crawling tasks. A comparative evaluation on real-world data also shows its interest as well as the performance of other available solutions.

The contributions of this paper are threefold: it references the software, features a benchmark, and provides a meaningful baseline for similar tasks. The tool performs significantly better than other open-source solutions in this evaluation and in external benchmarks.

Trafilatura: {A} Web Scraping Library and Command-Line Tool for Text Discovery and Extraction

To build automated simplification systems, corpora of complex sentences and their simplified versions is the first step to understand sentence complexity and enable the development of automatic text simplification systems. We present a lexical and syntactically simplified Urdu simplification corpus with a detailed analysis of the various simplification operations and human evaluation of corpus quality. We further analyze our corpora using text readability measures and present a comparison of the original, lexical simplified and syntactically simplified corpora. In addition, we compare our corpus with other existing simplification corpora by building simplification systems and evaluating these systems using BLEU and SARI scores. Our system achieves the highest BLEU score and comparable SARI score in comparison to other systems. We release our simplification corpora for the benefit of the research community.

Automatic Sentence Simplification in Low Resource Settings for Urdu

This paper examines relationships between online self-disclosure and social support during the COVID-19 crisis. A total of 2,399 posts and 30,073 comments are crawled from the r/COVID19_support subreddit. We manually extract fine-grained personal information categories and types of requested social support from a post. We further develop a novel AI-driven classifier to automatically identify types of social support offered in a comment. Our findings show that users tend to share their health and occupation related information most frequently. Variability in the types of information disclosed amongst informational and emotional support-seeking content is explored. Our analyses indicate that: 1) users are more likely to share their age, education, and location information when seeking both informational and emotional support, as opposed to pursuing either one; 2) while personal information sharing increases the probability of users receiving informational support when requested, it has no correlation with emotional support.

Effects of Online Self-Disclosure on Receiving Social Support During the COVID-19 Pandemic

Stereotypes are inferences drawn about people based on their demographic attributes, which may result in harms to users when a system is deployed.
In generative language-inference tasks, given a premise, a model produces plausible hypotheses that follow either logically (natural language inference) or common-sensically (commonsense inference).
Such tasks are therefore a fruitful setting in which to explore the degree to which NLP systems encode stereotypes.
In our work, we study how stereotypes manifest when the potential targets of stereotypes are situated in real-life, neutral contexts.
We collect human judgments on the presence of stereotypes in generated inferences and compare how perceptions of stereotypes vary due to annotator positionality.

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A Research Framework for Understanding Education-Occupation Alignment with NLP Techniques

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Targeting the Benchmark: On Methodology in Current Natural Language Processing Research

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