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Lexical ambiguity is a pervasive feature of natural language, and a major difficulty in understanding language is selecting the intended meaning when more than one are possible. Despite this difficulty, many studies have found a processing advantage for words with multiple related meanings. There are mixed results for words with multiple unrelated meanings. Our work investigates whether contextualized word embeddings are able to capture human-like distinctions between senses and meanings and whether they can predict human behavior. We reanalyze data from previous experiments and find that our method does replicate the polyseme advantage and homonym disadvantage previously reported. Additionally, our contin- uous predictors are superior to binary distinctions derived from lexicographic resources.

EMNLP 2022

Contextual Embeddings Can Distinguish Homonymy from Polysemy in a Human-Like Way

ambiguity advantage

lexical ambiguity

bert

workshop paper

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Garden path sentences (i.e. “the horse raced past the barn fell”) are sentences that readers initially incorrectly parse, requiring partial or total re-analysis of the sentence structure. Given human difficulty in parsing garden paths, we aim to compare transformer language models’ performance on these sentences. We assess a BERT question-answering model’s performance on comprehension questions based on garden path and control sentences. We then further investigate the semantic roles assigned to arguments of verbs in garden path and control sentences by utilizing a probe task to directly assess which semantic role(s) BERT assigns. We find that BERT has relatively low performance in question answering based on garden path contexts, and the model incorrectly assigns semantic roles, aligning for the most part with human performance.

The BERT Walked Down the Garden Path Assigned Semantic Roles

Automatic Speech Recognition (ASR) systems have become a very natural human-machine interaction in which it allows users to speak entries rather than punching numbers on a keypad. This study presents an ASR system for Amharic language that is one of the Ethiopian Languages. The current attempts on speech recognition systems require thousands of hours of transcribed speech dataset to reach adequate accuracy. However, the large majority of under-resourced spoken languages in general, Ethiopic languages in particular, have a very limited amount of labeled speech dataset. Thus, in this study, a self-supervised Transformer based Wave2Vec 2.0 approach has been conducted to build an ASR system. In order to pretrain the proposed model in an unsupervised approach, a total of more than 200 hours unlabeled speech have been collected. In addition, a total of 30 minutes of labeled speech dataset has been prepared. Then, a Wave2Vec model that is initialized with weighted parameters has been pre-trained on the unlabeled speech 
dataset. Then the pretrained model has been fine-tuned using a small amount of labeled speech dataset. A standard Word Error Rate (WER) evaluation metric has been used to evaluate the fine-tuned multilingual ASR model and it has 
shown a clear significant result.

Automatic Speech Recognition for Amharic Language using Self-Supervised

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.

Prosody Based Automatic Speech segmentation for Amharic

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.

Contextual Embeddings Can Distinguish Homonymy from Polysemy in a Human-Like Way

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The BERT Walked Down the Garden Path Assigned Semantic Roles

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