United Arab Emirates

In this paper, we introduce a conversational agent (chatbot) for Hindi-speaking youth called BELA—Bot for English Language Acquisition. Developed for young underprivileged students at an Indian non-profit, the agent supports both Hindi and Hinglish (code-switched Hindi and English, written primarily with English orthography) utterances. BELA has two interaction modes: a question-answering mode for classic English language learning tasks like word meanings, translations, reading passage comprehensions, etc., and an open-domain dialogue system mode to allow users to practice their language skills.

We present a high-level overview of the design of BELA, including the implementation details and the preliminary results of our early prototype. We also report the challenges in creating an English-language learning chatbot for a largely Hindi-speaking population.

EMNLP 2022

BELA: Bot for English Language Acquisition

large-language models

second language learning

dialogue systems

workshop paper

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The various potential of children can be limited by language delay or language impairments. However, there are many instances where parents are unaware of the child's condition and do not obtain appropriate treatment as a result.  Additionally, experts collecting children's utterance to establish norms of language tests and evaluating children's language development level takes a significant amount of time and work. To address these issues, dependable automated screening tools are required. In this paper, we used pretrained LM to assist experts in quickly and objectively screening the language development level of children. Here, evaluating the language development level is to ensure that the child has the appropriate language abilities for his or her age, which is the same as the child's age. To do this, we analyzed the utterances of children according to age. Based on these findings, we use the standard deviations of the pretrained LM's probability as a score for children to screen their language development level. The experiment results showed very strong correlations between our proposed method and the Korean language test REVT (REVT-R, REVT-E), with Pearson correlation coefficient of 0.9888 and 0.9892, respectively.

Applicability of Pretrained Language Models: Automatic Screening for Children's Language Development Level

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Transformers-Based Approach for a Sustainability Term-Based Sentiment Analysis (STBSA)

Hateful memes are a growing menace on social media. While the image and its corresponding text in a meme are related, they do not necessarily convey the same meaning when viewed individually. Hence, detecting hateful memes requires careful consideration of both visual and textual information. Multimodal pre-training can be beneficial for this task because it effectively captures the relationship between the image and the text by representing them in a similar feature space. Furthermore, it is essential to model the interactions between the image and text features through intermediate fusion. Most existing methods either employ multimodal pre-training or intermediate fusion, but not both. In this work, we propose the Hate-CLIPper architecture, which explicitly models the cross-modal interactions between the image and text representations obtained using Contrastive Language-Image Pre-training (CLIP) encoders via a feature interaction matrix (FIM). A simple classifier based on the FIM representation is able to achieve state-of-the-art performance on the Hateful Memes Challenge (HMC) dataset with an AUROC of 85.8, which even surpasses the human performance of 82.65. Experiments on other meme datasets such as Propaganda Memes and TamilMemes also demonstrate the generalizability of the proposed approach. Finally, we analyze the interpretability of the FIM representation and show that cross-modal interactions can indeed facilitate the learning of meaningful concepts. The code for this work is available at https://github.com/gokulkarthik/hateclipper

Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP Features

In this paper, we describe a participatory, collaborative, and cost-effective process for creating translations in Oshiwambo, the most widely spoken African language in Namibia. We aim to (1) build a resource for language technology development, (2) bridge generational gaps in cultural and language knowledge, and at the same time (3) provide socio-economic opportunities through language preservation. The created data spans diverse topics of cultural importance, and comprises over 7.5k sentences written in the Oshindonga dialect and translated to English, the largest parallel corpus for Oshiwambo to-date. We show that it is very effective for machine translation, especially when combined with transfer learning.

Participatory Translations of Oshiwambo: Towards Culture Preservation with Language Technology

In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world. It provokes a surge of incomplete, biased, and incorrect information, also known as an infodemic. This global infodemic jeopardizes measures to control the pandemic by creating panic, vaccine hesitancy, and fragmented social response. Platforms like Facebook allow advertisers to adapt their messaging to target different demographics and help alleviate or exacerbate the infodemic problem depending on their content. In this paper, we propose a minimally supervised multi-task learning framework for understanding messaging on Facebook related to the covid vaccine by identifying ad themes and moral foundations. Furthermore, we perform a more nuanced thematic analysis of messaging tactics of vaccine campaigns on social media so that policymakers can make better decisions on pandemic control.

Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision

Gender Bias in Meta-Embeddings

Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media

Encoded text representations often capture sensitive attributes about individuals (e.g., race or gender), which raise privacy concerns and can make downstream models unfair to certain groups. In this work, we propose FEDERATE, an approach that combines ideas from differential privacy and adversarial training to learn private text representations which also induces fairer models. We empirically evaluate the trade-off between the privacy of the representations and the fairness and accuracy of the downstream model on four NLP datasets. Our results show that FEDERATE consistently improves upon previous methods, and thus suggest that privacy and fairness can positively reinforce each other.

Fair NLP Models with Differentially Private Text Encoders

An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences. However, the degree of explicitness of a generated statement that can cause physical harm varies. In this paper, we distinguish types of text that can lead to physical harm and establish one particularly underexplored category: covertly unsafe text. Then, we further break down this category with respect to the system's information and discuss solutions to mitigate the generation of text in each of these subcategories. Ultimately, our work defines the problem of covertly unsafe language that causes physical harm and argues that this subtle yet dangerous issue needs to be prioritized by stakeholders and regulators. We highlight mitigation strategies to inspire future researchers to tackle this challenging problem and help improve safety within smart systems.

Mitigating Covertly Unsafe Text within Natural Language Systems

Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate). Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument. We find that existing pretrained large language models perform poorly on this task. In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% F1 scores on Logic and 4.51% on LogicClimate. We encourage future work to explore this task since (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. Our dataset and code are available at https:// github.com/causalNLP/logical-fallacy

BELA: Bot for English Language Acquisition

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Applicability of Pretrained Language Models: Automatic Screening for Children's Language Development Level

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