Dominican Republic

Code-mixed text generation systems have found applications in many downstream tasks, including speech recognition, translation and dialogue. A paradigm of these generation systems relies on well-defined grammatical theories of code-mixing, and there is a lack of comparison of these theories. We present a large-scale human evaluation of two popular grammatical theories, Matrix-Embedded Language (ML) and Equivalence Constraint (EC). We compare them against three heuristic-based models and quantitatively demonstrate the effectiveness of the two grammatical theories.

EMNLP 2021

Comparing Grammatical Theories of Code-Mixing

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workshop paper

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EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Fine-grained temporal relation extraction (FineTempRel) aims to recognize the durations and timeline of event mentions in text. A missing part in the current deep learning models for FineTempRel is their failure to exploit the syntactic structures of the input sentences to enrich the representation vectors. In this work, we propose to fill this gap by introducing novel methods to integrate the syntactic structures into the deep learning models for FineTempRel. The proposed model focuses on two types of syntactic information from the dependency trees, i.e., the syntax-based importance scores for representation learning of the words and the syntactic connections to identify important context words for the event mentions. We also
present two novel techniques to facilitate the knowledge transfer between the subtasks of FineTempRel, leading to a novel model with the state-of-the-art performance for this task.

Fine-grained Temporal Relation Extraction with Ordered-Neuron LSTM and Graph Convolutional Networks

WARNING: This article contains contents that may offend the readers.

Strategies that insert intentional noise into text when posting it are commonly observed in the online space, and sometimes they aim to let only certain community users understand the genuine semantics. In this paper, we explore the purpose of such actions by categorizing them into tricks, memes, fillers, and codes, and organize the linguistic strategies that are used for each purpose. Through this, we identify that such strategies can be conducted by authors for multiple purposes, regarding the presence of stakeholders such as `Peers' and `Others'. We finally analyze how these strategies appear differently in each circumstance, along with the unified taxonomy accompanying examples.

Google-trickers, Yaminjeongeum, and Leetspeak: An Empirical Taxonomy for Intentionally Noisy User-Generated Text

ICD-9 coding is a relevant clinical billing task,
where unstructured texts with information
about a patient’s diagnosis and treatments are
annotated with multiple ICD-9 codes. Automated
ICD-9 coding is an active research field,
where CNN- and RNN-based model architectures
represent the state-of-the-art approaches.
In this work, we propose a description-based
label attention classifier to improve the model
explainability when dealing with noisy texts
like clinical notes. We evaluate our proposed
method with different transformer-based encoders
on the MIMIC-III-50 dataset. Our
method achieves strong results together with
augmented explainability.

Description-based Label Attention Classifier for Explainable ICD-9 Classification

Language models used in speech recognition are often either evaluated intrinsically using perplexity on test data, or extrinsically with an automatic speech recognition (ASR) system. The former evaluation does not always correlate well with ASR performance, while the latter could be specific to particular ASR systems. Recent work proposed to evaluate language models by using them to classify ground truth sentences among alternative phonetically similar sentences generated by a fine state transducer. Underlying such an evaluation is the assumption that the generated sentences are linguistically incorrect. In this paper, we first put this assumption into question, and observe that alternatively generated sentences could often be linguistically correct when they differ from the ground truth by only one edit. Secondly, we showed that by using multi-lingual BERT, we can achieve better performance than previous work on two code-switching data sets.
Our implementation is publicly available on Github.

Intrinsic evaluation of language models for code-switching

Multimodal named entity recognition (MNER) requires to bridge the gap between language understanding and visual context. While many multimodal neural techniques have been proposed to incorporate images into the MNER task, the model's ability to leverage multimodal interactions remains poorly understood. In this work, we conduct in-depth analyses of existing multimodal fusion techniques from different perspectives and describe the scenarios where adding information from the image does not always boost performance. We also study the use of captions as a way to enrich the context for MNER. Experiments on three datasets from popular social platforms expose the bottleneck of existing multimodal models and the situations where using captions is beneficial.

Can images help recognize entities? A study of the role of images for Multimodal NER

State-of-the-art approaches to spelling error correction problem include Transformer-based Seq2Seq models, which require large training sets and suffer from slow inference time; and sequence labeling models based on Transformer encoders like BERT, which involve token-level label space and therefore a large pre-defined vocabulary dictionary. In this paper we present a Hierarchical Character Tagger model, or HCTagger, for short text spelling error correction. We use a pre-trained language model at the character level as a text encoder, and then predict character-level edits to transform the original text into its error-free form with a much smaller label space. For decoding, we propose a hierarchical multi-task approach to alleviate the issue of long-tail label distribution without introducing extra model parameters. Experiments on two public misspelling correction datasets demonstrate that HCTagger is an accurate and much faster approach than many existing models.

Hierarchical Character Tagger for Short Text Spelling Error Correction

Large-scale language models such as ELMo and BERT have pushed the horizon of what is possible in semantic role labeling (SRL), solving the out-of-vocabulary problem and enabling end-to-end systems, but they have also introduced significant biases. We evaluate three SRL parsers on very simple transitive sentences with verbs usually associated with animate subjects and objects, such as 
"Mary babysat Tom": a state-of-the-art parser based on BERT, an older parser based on GloVe, and an even older parser from before the days of word embeddings. When arguments are word forms predominantly used as person names, aligning with common sense expectations of animacy, the BERT-based parser is unsurprisingly superior; yet, with abstract or random nouns, the opposite picture emerges. We refer to this as "common sense bias" and present a challenge dataset for evaluating the extent to which parsers are sensitive to such a bias. Our code and challenge dataset are available

Common Sense Bias in Semantic Role Labeling

WhatsApp Messenger is one of the most popular channels for spreading information with a current reach of more than 180 countries and 2 billion people. Its widespread usage has made it one of the most popular media for information propagation among the masses during any socially engaging event. In the recent past, several countries have witnessed its effectiveness and influence in political and social campaigns. We observe a high surge in information and propaganda flow during election campaigning. In this paper, we explore a high-quality large-scale user-generated dataset curated from WhatsApp comprising of 281 groups, 31,078 unique users, and 223,404 messages shared before, during, and after the Indian General Elections 2019, encompassing all major Indian political parties and leaders. In addition to the raw noisy user-generated data, we present a fine-grained annotated dataset of 3,848 messages that will be useful to understand the various dimensions of WhatsApp political campaigning. We present several complementary insights into the investigative and sensational news stories from the same period. Exploratory data analysis and experiments showcase several exciting results and future research opportunities.

PoliWAM: An Exploration of a Large Scale Corpus of Political Discussions on WhatsApp Messenger

Due to unstructured sentences and some misspellings and errors, finding the named entities in a noisy environment such as social media takes much more effort than formal languages. The ParsTwiNER corpus, gathered from Persian Twitter, contains about 250k tokens, based on standard instructions such as MUC-6 or CoNLL 2003. Using Cohen's Kappa coefficient, the consistency of annotators is 0.95, a high score. We demonstrate that some state-of-the-art models were degraded on this corpus and trained a model using parallel transfer learning based on BERT architecture. The proposed model works well in informal Persian, as well as in formal Persian. The corpus and trained models are publicly available at https://github.com/overfit-ir/parstwiner and https://huggingface.co/overfit/twiner-bert-base-mtl, respectively.

ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian

We present DreamDrug, a crowdsourced dataset for detecting mentions of drugs in
noisy user-generated item listings from darknet markets. Our dataset contains nearly 15,000 manually annotated drug entities in over 3,500 item listings scraped from the darknet market platform "DreamMarket" in 2017. We also train and evaluate baseline models for detecting these entities, using contextual language
models fine-tuned in a few-shot setting and on the full dataset, and examine the effect of pretraining on in-domain unannotated corpora.

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Fine-grained Temporal Relation Extraction with Ordered-Neuron LSTM and Graph Convolutional Networks

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