Dominican Republic

Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations.
However, despite the recent advances in NLP, it is currently unknown if such models are robust in face of negation, which is pervasive across language varieties.

In this paper we evaluate three state-of-the-art systems,
showing their fragility against negation, and then we introduce two possible strategies to increase the robustness of these models: a pipeline approach, relying on a specific component for negation detection; an augmentation of an ADE extraction dataset to artificially create negated samples and further train the models.

We show that both strategies bring significant increases in performance, lowering the number of spurious entities predicted by the models.
Our dataset and code will be publicly released to encourage research on the topic.

EMNLP 2021

NADE: A Benchmark for Robust Adverse Drug Events Extractionin Face of Negations

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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.

We studied structure augmentation on documents for keyphrase generation.

Structure-Augmented Keyphrase Generation

Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET). However, there lacks a comprehensive understanding about how to make better use of the existing information sources and how they affect the performance of ZFET. In this paper, we empirically study three kinds of auxiliary information: {context consistency}, {type hierarchy} and {background knowledge} (e.g., prototypes and descriptions) of types, and propose a multi-source fusion model (MSF) targeting these sources. The performance obtains up to 11.42% and 22.84% absolute gains over state-of-the-art baselines on BBN and Wiki respectively with regard to macro F1 scores. More importantly, we further discuss the characteristics, merits and demerits of each information source and provide an intuitive understanding of the complementarity among them.

An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing

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.

DreamDrug - A crowdsourced NER dataset for detecting drugs in darknet markets

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.

Comparing Grammatical Theories of Code-Mixing

The benchmark performance of cross-database semantic parsing has climbed steadily in recent years, catalyzed by the wide adoption of pre-trained language models. Yet existing work have shown that state-of-the-art cross-database semantic parsers struggle to generalize to novel user utterances, databases and query structures. To obtain transparent details on the strengths and limitation of these models, we propose a diagnostic testing approach based on controlled synthesis of canonical natural language and SQL pairs. Inspired by the "Checklist" paper (Ribeiro et al. 2020), we characterize a set of essential capabilities for cross-database semantic parsing models, and detailed the method for synthesizing the corresponding test data. We evaluated a variety of high performing models using the proposed approach, and identified several non-obvious weaknesses across models (e.g. unable to correctly select many columns). Our dataset and code are released as a test suite at: http://github.com/hclent/BehaviorCheckingSemPar.

Improving Punctuation Restoration for Speech Transcripts via External Data

Fake news causes significant damage to society. To deal with these fake news, several studies on building detection models and arranging datasets have been conducted.
Most of the fake news datasets depend on a specific time period. Consequently, the detection models trained on such a dataset have difficulty detecting novel fake news generated by political changes and social changes; they may possibly result in biased output from the input, including specific person names and organizational names. We refer to this problem as Diachronic Bias because it is caused by the creation date of news in each dataset. In this study, we confirm the bias, especially proper nouns including person names, from the deviation of phrase appearances in each dataset. Based on these findings, we propose masking methods using Wikidata to mitigate the influence of person names and validate whether they make fake news detection models robust through experiments with in-domain and out-of-domain data.

Mitigation of Diachronic Bias in Fake News Detection Dataset

This work takes a critical look at the evaluation of user-generated content (UGC) automatic translation and the well-known specificities of which raise many challenges for MT. We introduce the PMUMT, a new data set for UGC translation evaluation. Using this data set, we conduct several experiments to measure the impact of different kinds of UGC specificities on translation quality, more precisely than previously possible.

Understanding the Impact of UGC Specificities on Translation Quality

This work explores the capacities of character-based Neural Machine Translation to translate noisy User-Generated Content (UGC). Within a strict zero-shot scenario, we first study the detrimental impact on translation performance of various UGC phenomena on an annotated dataset we developed, and then show that such models are indeed incapable of handling unknown characters, for which we propose a simple but efficient solution.

Noisy User-Generated Content Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models

Twitter data has become established as a valuable source of data for various application scenarios in the past years. For many such applications, it is necessary to know where Twitter posts (tweets) were sent from or what location they refer to. Researchers have frequently used exact coordinates provided in a small percentage of tweets, but Twitter removed the option to share these coordinates in mid-2019. Moreover, there is reason to suspect that a large share of the provided coordinates did not correspond to GPS coordinates of the user even before that.

In this paper, we explain the situation and the 2019 policy change and shed light on the various options of still obtaining location information from tweets. We provide usage statistics including changes over time, and analyze what the removal of exact coordinates means for various common research tasks performed with Twitter data. Finally, we make suggestions for future research requiring geolocated tweets.

Changes in Twitter geolocations: Insights and suggestions for future usage

Despite excellent performance on tasks such as question answering, Transformer-based architectures remain sensitive to syntactic and contextual ambiguities. Question Paraphrasing (QP) offers a promising solution as a means to augment existing datasets. The main challenges of current QP models include lack of training data and difficulty in generating diverse and natural questions. In this paper, we present ConQuest, a framework for generating synthetic datasets for contextual question paraphrasing. To this end, ConQuest first employs an answer-aware question generation(QG) model to create a question-pair dataset and then uses this data to train a contextualized question paraphrasing model. We extensively evaluate ConQuestand to show its ability to produce more diverse and fluent question pairs than existing approaches. Our contextual paraphrase model also establishes a strong baseline for end-to-end contextual paraphrasing. Further, We find that context can improve BLEU-1 score on contextual compression and expansion by 4.3 and 11.2 respectively, compared to a non-contextual model.

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NADE: A Benchmark for Robust Adverse Drug Events Extractionin Face of Negations

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