United Arab Emirates

The main goal of this work is to develop sentence level automatic speech segmentation system for Amharic. Sentence segmentation is a process of identifying the end of a sentence. In this study, sentence segmentation system is 
implemented in to two approaches. In the first approach, we used an automatic tool for segmenting and labeling of Amharic speech data. Acoustic model is created using 
speech and their text scripts and compiling them into a statistical representation of sounds which makeup words. This is done through HMM modeling. The approach one automatic speech segmentation system is done by forced alignment. In this approach we used rule-based and AdaBoost to discriminate the true boundaries from false. In the second approach, we extracted prosodic features directly 
from speech waveform and also statistical method, AdaBoost, is used.

EMNLP 2022

Prosody Based Automatic Speech segmentation for Amharic

acoustic model

automatic speech segmentation

prosody

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Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

The way sexual abuse is represented in the media can drive public opinion. We use NLP techniques to analyze representation of sexual abuse in New York Times (NYT) articles over three time periods, ending with the era of MeToo. We use computational methods to surface sentences in articles involving victims and perpetrators, which we annotate for victim and perpetrator demographic information. We use power and agency connotation frames to measure if and how ascribed power and agency of victims and perpetrators in news articles have changed over the years. We find that women are ascribed more agency in sentences describing abuse of women victims over time, and power ascribed to male perpetrators has decreased over time.

Before and beyond MeToo: Measuring changes in power and agency within sexual abuse news stories over time

In this work, we propose a flexible multimodal transformer architecture for classifying depression from social media posts containing text and images. We show that our model is able to surpass other methods by a large margin on a popular multimodal depression dataset from Twitter, obtaining state-of-the-art results of 95\% F1 score. Our model is flexible and can easily incorporate temporal information by manipulating the transformer positional encodings. Moreover, it can operate both on randomly sampled and unordered sets of posts, to be more robust to dataset noise, and on ordered collections of posts, accommodating the relative posting intervals without any major architectural modifications.

Detecting Depression on Twitter with a Time-Aware Multimodal Transformer

Out-of-Vocabulary (OOV) is a problem for Neural Machine Translation (NMT). OOV refers to words with a low occurrence in the training data or those absent from the training data. Word or phrase-based Data Augmentation (DA) techniques have been used to alleviate this. However, existing DA techniques have addressed only one of these OOV types and are limited to considering either syntactic or semantic constraints. We present a word/phrase replacement-based DA technique that considers both types of OOV, considering both syntactic and semantic properties of the words to guarantee fluency in the synthetic sentences. This technique was experimented with low resource language pair Sinhala-English.

Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation

Microaggressions (MAs) are subtle, often unintentional, form of prejudice. They often impact people of minority or marginalized backgrounds. Gender MAs are common in India but not well understood. To the best of my knowledge, there is no existing MAs dataset in Hindi. To tackle the problem of MAs, a dataset needs to be created with which a machine learning model can be trained. Enabling a model to learn MAs would allow for MAs detection and possibly correction in text. In this paper, I build (i) a small gender MAs dataset and crowdsource its labeling, and (ii) a novel pipeline to detect MAs in Hindi.

Tackling Gender Microaggressions in Hindi Text

Lexicon-based method for sentiment analysis provides explainable results compared to deep learning methods. However, the approach does not capture contextual information and provides sub-optimal performance. In this paper, we build domain-specific lexicons and shifter patterns that capture contextual information. We show that symbolic approaches can achieve competitive performance as deep neural networks for sentiment classification tasks with limited data. Our approach is useful in cases where an explanation is the most important criterion and not the accuracy. 



Domain-Specific Lexicon-Based Sentiment Analysis using Contextual Shifter Patterns

Large language models appear to learn facts from the large text corpora they are trained on. Such facts are encoded implicitly within their many parameters, making it difficult to verify or manipulate what knowledge has been learned. Language models have recently been extended to multilingual language models (MLLMs), enabling knowledge to be learned across hundreds of languages. Meanwhile, knowledge graphs contain facts in an explicit triple format, which require careful and costly curation and are only available in a few high-resource languages, restricting their research and application. To address these issues, we propose to enhance MLLMs with knowledge from multilingual knowledge graphs (MLKGs) so as to tackle language and knowledge graph tasks across many languages, including low-resource ones. Specifically, we introduce a lightweight adapter set to enhance MLLMs with cross-lingual entity alignment and facts from MLKGs for many languages. Experiments on common benchmarks show that such enhancement benefits both MLLMs and MLKGs, achieving: (1) comparable or improved performance for knowledge graph completion and entity alignment relative to baselines, especially for low-resource languages (for which knowledge graphs are unavailable); and (2) improved MLLM performance on language understanding tasks that require multilingual factual knowledge; all while maintaining performance on other general language tasks.

Adapters for Enhanced Modeling of Multilingual Knowledge and Text

Bilingual lexicon induction (BLI) with limited bilingual supervision is a crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI methods rely on the induction of cross-lingual word embeddings (CLWEs) to capture cross-lingual word similarities; such CLWEs are obtained 1) via traditional static models (e.g., VecMap), or 2) by extracting type-level CLWEs from multilingual pretrained language models (mPLMs), or 3) through combining the former two options. In this work, we propose a novel semi-supervised post-hoc reranking method termed BLICEr (BLI with Cross-Encoder Reranking), applicable to any precalculated CLWE space, which improves their BLI capability. The key idea is to 'extract' cross-lingual lexical knowledge from mPLMs, and then combine it with the original CLWEs. This crucial step is done via 1) creating a word similarity dataset, comprising positive word pairs (i.e., true translations) and hard negative pairs induced from the original CLWE space, and then 2) fine-tuning an mPLM (e.g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores. At inference, we 3) combine the similarity score from the original CLWE space with the score from the BLI-tuned cross-encoder. BLICEr establishes new state-of-the-art results on two standard BLI benchmarks spanning a wide spectrum of diverse languages: it substantially outperforms a series of strong baselines across the board. We also validate the robustness of BLICEr with different CLWEs. Our code is available at https://github.com/cambridgeltl/BLICEr.

Improving Bilingual Lexicon Induction with Cross-Encoder Reranking

In an effort to study the inductive biases of language models, numerous studies have attempted to use linguistically motivated tasks as a proxy of sorts, wherein performance on these tasks would imply an inductive bias towards a specific linguistic phenomenon. In this study, we attempt to analyse the inductive biases of language models with respect to natural language phenomena
in the context of building multilingual embedding spaces.
We sample corpora from 2 sources in 15 languages and train language models on pseudo-bilingual variants of each corpus, created by duplicating each corpus and shifting token indices for half the resulting corpus. We evaluate the cross-lingual capabilities of these LMs, and show that while correlations with language families tend to be weak, other corpus-level characteristics, such as type-token ratio, tend to be more strongly correlated. Finally, we show that multilingual spaces can be built, albeit less effectively, even when additional destructive perturbations are applied to the training corpora, implying that (effectively) bag-of-words models also have an inductive bias that is sufficient for inducing multilingual spaces.

The Effects of Corpus Choice and Morphosyntax on Multilingual Space Induction

Most vision-and-language pretraining research focuses on English tasks. However, the creation of multilingual multimodal evaluation datasets (e.g. Multi30K, xGQA, XVNLI, and MaRVL) poses a new challenge in finding high-quality training data that is both multilingual and multimodal. We investigate whether machine translating English multimodal data can be an effective proxy for the lack of readily available multilingual data. We call this framework TD-MML: Translated Data for Multilingual Multimodal Learning, and it can be applied to any multimodal dataset and model. We apply it to both pretraining and fine-tuning data with a state-of-the-art model. In order to prevent models from learning from low-quality translated text, we propose two metrics for automatically removing such translations from the resulting datasets. In experiments on five tasks across 20 languages in the IGLUE benchmark, we show that translated data can provide a useful signal for multilingual multimodal learning, both at pretraining and fine-tuning.

Multilingual Multimodal Learning with Machine Translated Text

Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release HumSet, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, HumSet provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. HumSet also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of experiments on Pre-trained Language Models (PLM) to establish strong baselines for future research in this domain. The dataset is available at https://blog.thedeep.io/humset/.

Next from EMNLP 2022

Before and beyond MeToo: Measuring changes in power and agency within sexual abuse news stories over time

Similar lecture

Enhancing Amharic Sentence Segmentation with Prosodic Features and Neural Network Models

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