Reviewerly/exHarmony huggingface dataset:
Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning

This paper shines a light on the potential of definition-based semantic models for detecting idiomatic and semi-idiomatic multiword expressions (MWEs) in clinical terminology. Our study focuses on biomedical entities defined in the UMLS ontology and aims to help prioritize the translation efforts of these entities. In particular, we develop an effective tool for scoring the idiomaticity of biomedical MWEs based on the degree of similarity between the semantic representations of those MWEs and a weighted average of the representation of their constituents. We achieve this using a biomedical language model trained to produce similar representations for entity names and their definitions, called BioLORD. The importance of this definition-based approach is highlighted by comparing the BioLORD model to two other state-of-the-art biomedical language models based on Transformer: SapBERT and CODER. Our results show that the BioLORD model has a strong ability to identify idiomatic MWEs, not replicated in other models. Our corpus-free idiomaticity estimation helps ontology translators to focus on more challenging MWEs.
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision

Contrastive pre-training on distant supervision has shown remarkable effectiveness in improving supervised relation extraction tasks. However, the existing methods ignore the intrinsic noise of distant supervision during the pre-training stage. In this paper, we propose a weighted contrastive learning method by leveraging the supervised data to estimate the reliability of pre-training instances and explicitly reduce the effect of noise.Experimental results on three supervised datasets demonstrate the advantages of our proposed weighted contrastive learning approach compared to two state-of-the-art non-weighted baselines.Our code and models are available at: https://github.com/YukinoWan/WCL.
Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task Dialogues

Reinforcement learning has been widely adopted to model dialogue managers in task-oriented dialogues. However, the user simulator provided by state-of-the-art dialogue frameworks are only rough approximations of human behaviour. The ability to learn from a small number of human interactions is hence crucial, especially on multi-domain and multi-task environments where the action space is large. We therefore propose to use structured policies to improve sample efficiency when learning on these kinds of environments. We also evaluate the impact of learning from human vs simulated experts. Among the different levels of structure that we tested, the graph neural networks (GNNs) show a remarkable superiority by reaching a success rate above 80% with only 50 dialogues when learning from simulated experts. They also show superiority when learning from human experts, although a performance drop was observed. We therefore suggest to concentrate future research efforts on bridging the gap between human data, simulators and automatic evaluators in dialogue frameworks.
Towards Fine-tuning Pre-trained Language Models with Integer Forward and Backward Propagation

Mohammadreza Tayaranian Hosseini, Alireza Ghaffari, Marzieh S. Tahaei, Mehdi Rezagholizadeh, Masoud Asgharian, Vahid Partovi Nia. Findings of the Association for Computational Linguistics: EACL 2023. 2023.
Learning to Retrieve Engaging Follow-Up Queries

Open domain conversational agents can answer a broad range of targeted queries. However, the sequential nature of interaction with these systems makes knowledge exploration a lengthy task which burdens the user with asking a chain of well phrased questions. In this paper, we present a retrieval based system and associated dataset for predicting the next questions that the user might have. Such a system can proactively assist users in knowledge exploration leading to a more engaging dialog. The retrieval system is trained on a dataset called the Follow-up Query Bank (FQ-Bank). FQ-Bank contains ~14K multi-turn information-seeking conversations with a valid follow-up question and a set of invalid candidates. The invalid candidates are generated to simulate various syntactic and semantic confounders such as paraphrases, partial entity match, irrelevant entity, and ASR errors. We use confounder specific techniques to simulate these negative examples on the OR-QuAC dataset. Then, we train ranking models on FQ-Bank and present results comparing supervised and unsupervised approaches. The results suggest that we can retrieve the valid follow-ups by ranking them in higher positions compared to confounders, but further knowledge grounding can improve ranking performance.FQ-Bank is publicly available at https://github.com/amazon-science/fq-bank.
Exploring Enhanced Code-Switched Noising for Pretraining in Neural Machine Translation

Multilingual pretraining approaches in Neural Machine Translation (NMT) have shown that training models to denoise synthetic code-switched data can yield impressive performance gains — owing to better multilingual semantic representations and transfer learning. However, they generated the synthetic code-switched data using non-contextual, one-to-one word translations obtained from lexicons - which can lead to significant noise in a variety of cases, including the poor handling of polysemes and multi-word expressions, violation of linguistic agreement and inability to scale to agglutinative languages. To overcome these limitations, we propose an approach called Contextual Code-Switching (CCS), where contextual, many-to-many word translations are generated using a ‘base’ NMT model. We conduct experiments on 3 different language families - Romance, Uralic, and Indo-Aryan - and show significant improvements (by up to 5.5 spBLEU points) over the previous lexicon-based SOTA approaches. We also observe that small CCS models can perform comparably or better than massive models like mBART50 and mRASP2, depending on the size of data provided. We empirically analyse several key factors responsible for these - including context, many-to-many substitutions, code-switching language count etc. - and prove that they all contribute to enhanced pretraining of multilingual NMT models.
Text-Derived Knowledge Helps Vision: A Simple Cross-modal Distillation for Video-based Action Anticipation

Anticipating future actions in a video is useful for many autonomous and assistive technologies. Prior action anticipation work mostly treat this as a vision modality problem, where the models learn the task information primarily from the video features in the action anticipation datasets. However, knowledge about action sequences can also be obtained from external textual data. In this work, we show how knowledge in pretrained language models can be adapted and distilled into vision based action anticipation models. We show that a simple distillation technique can achieve effective knowledge transfer and provide consistent gains on a strong vision model (Anticipative Vision Transformer) for two action anticipation datasets (3.5% relative gain on EGTEA-GAZE+ and 7.2% relative gain on EPIC-KITCHEN 55), giving a new state-of-the-art result.
Double Retrieval and Ranking for Accurate Question Answering

Recent work has shown that an answer verification step introduced in Transformer-based answer selection models can significantly improve the state of the art in Question Answering. This step is performed by aggregating the embeddings of top k answer candidates to support the verification of a target answer. Although the approach is intuitive and sound, it still shows two limitations: (i) the supporting candidates are ranked only according to the relevancy with the question and not with the answer, and (ii) the support provided by the other answer candidates is suboptimal as these are retrieved independently of the target answer. In this paper, we address both drawbacks by proposing (i) a double reranking model, which, for each target answer, selects the best support; and (ii) a second neural retrieval stage designed to encode question and answer pair as the query, which finds more specific verification information. The results on well-known datasets for Answer Sentence Selection show significant improvement over the state of the art.
Lessons Learned from a Citizen Science Project for Natural Language Processing

Jan-Christoph Klie, Ji-Ung Lee, Kevin Stowe, Gözde Şahin, Nafise Sadat Moosavi, Luke Bates, Dominic Petrak, Richard Eckart De Castilho, Iryna Gurevych. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Task and Sentiment Adaptation for Appraisal Tagging

The Appraisal framework in linguistics defines the framework for fine-grained evaluations and opinions and has contributed to sentiment analysis and opinion mining. As developing appraisal-annotated resources requires tagging of several dimensions with distinct semantic taxonomies, it has been primarily conducted manually by human experts through expensive and time-consuming processes. In this paper, we study how to automatically identify and annotate text segments for appraisal. We formulate the problem as a sequence tagging problem and propose novel task and sentiment adapters based on language models for appraisal tagging. Our model, named Adaptive Appraisal (Aˆ2), achieves superior performance than baseline adapter-based models and other neural classification models, especially for cross-domain and cross-language settings. Source code for Aˆ2 is available at: https://github.com/ltian678/AA-code.git
How Far Can It Go? On Intrinsic Gender Bias Mitigation for Text Classification

To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics.Considering that the end use of these language models is for downstream tasks like text classification, it is important to understand how these intrinsic bias mitigation strategies actually translate to fairness in downstream tasks and the extent of this.In this work, we design a probe to investigate the effects that some of the major intrinsic gender bias mitigation strategies have on downstream text classification tasks.We discover that instead of resolving gender bias, intrinsic mitigation techniques and metrics are able to hide it in such a way that significant gender information is retained in the embeddings.Furthermore, we show that each mitigation technique is able to hide the bias from some of the intrinsic bias measures but not all, and each intrinsic bias measure can be fooled by some mitigation technique, but not all.We confirm experimentally, that none of the intrinsic mitigation techniques used without any other fairness intervention is able to consistently impact extrinsic bias.We recommend that intrinsic bias mitigation techniques should be combined with other fairness interventions for downstream tasks.
Probabilistic Robustness for Data Filtering

We introduce our probabilistic robustness rewarded data optimization (PRoDO) approach as a framework to enhance the model’s generalization power by selecting training data that optimizes our probabilistic robustness metrics. We use proximal policy optimization (PPO) reinforcement learning to approximately solve the computationally intractable training subset selection problem. The PPO’s reward is defined as our (𝛼,𝜖, 𝛾)-Robustness that measures performance consistency over multiple domains by simulating unknown test sets in real-world scenarios using a leaving-one-out strategy. We demonstrate that our PRoDO effectively filters data that lead to significantly higher prediction accuracy and robustness on unknown-domain test sets. Our experiments achieve up to +17.2% increase of accuracy (+25.5% relatively) in sentiment analysis, and -28.05 decrease of perplexity (-32.1% relatively) in language modeling.In addition, our probabilistic (𝛼,𝜖, 𝛾)-Robustness definition serves as an evaluation metric with higher levels of agreement with human annotations than typical performance-based metrics.
Bootstrapping Multilingual Semantic Parsers using Large Language Models

Abhijeet Awasthi, Nitish Gupta, Bidisha Samanta, Shachi Dave, Sunita Sarawagi, Partha Talukdar. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
MetaQA: Combining Expert Agents for Multi-Skill Question Answering

The recent explosion of question-answering (QA) datasets and models has increased the interest in the generalization of models across multiple domains and formats by either training on multiple datasets or combining multiple models. Despite the promising results of multi-dataset models, some domains or QA formats may require specific architectures, and thus the adaptability of these models might be limited. In addition, current approaches for combining models disregard cues such as question-answer compatibility. In this work, we propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores to select the best answer among a list of answer predictions. Through quantitative and qualitative experiments, we show that our model i) creates a collaboration between agents that outperforms previous multi-agent and multi-dataset approaches, ii) is highly data-efficient to train, and iii) can be adapted to any QA format. We release our code and a dataset of answer predictions from expert agents for 16 QA datasets to foster future research of multi-agent systems.
Question-Answer Sentence Graph for Joint Modeling Answer Selection

This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learning, our model constructs a small-scale relevant training graph per question in an unsupervised manner, and integrates with Graph Neural Networks. Graph nodes are question sentence to answer sentence pairs. We train and integrate state-of-the-art (SOTA) models for computing scores between question-question, question-answer, and answer-answer pairs, and use thresholding on relevance scores for creating graph edges. Online inference is then performed to solve the AS2 task on unseen queries. Experiments on two well-known academic benchmarks and a real-world dataset show that our approach consistently outperforms SOTA QA baseline models.
Meta Self-Refinement for Robust Learning with Weak Supervision

Training deep neural networks (DNNs) under weak supervision has attracted increasing research attention as it can significantly reduce the annotation cost. However, labels from weak supervision can be noisy, and the high capacity of DNNs enables them to easily overfit the label noise, resulting in poor generalization. Recent methods leverage self-training to build noise-resistant models, in which a teacher trained under weak supervision is used to provide highly confident labels for teaching the students. Nevertheless, the teacher derived from such frameworks may have fitted a substantial amount of noise and therefore produce incorrect pseudo-labels with high confidence, leading to severe error propagation. In this work, we propose Meta Self-Refinement (MSR), a noise-resistant learning framework, to effectively combat label noise from weak supervision. Instead of relying on a fixed teacher trained with noisy labels, we encourage the teacher to refine its pseudo-labels. At each training step, MSR performs a meta gradient descent on the current mini-batch to maximize the student performance on a clean validation set. Extensive experimentation on eight NLP benchmarks demonstrates that MSR is robust against label noise in all settings and outperforms state-of-the-art methods by up to 11.4% in accuracy and 9.26% in F1 score.
A Multi-dimensional Evaluation of Tokenizer-free Multilingual Pretrained Models

Recent works on tokenizer-free multilingual pretrained models show promising results in improving cross-lingual transfer and reducing engineering overhead compared to subword-based alternatives.However, previous work mainly focuses on reporting accuracy on a limited set of tasks and data settings, placing less emphasis on other important factors when tuning and deploying the models in practice, such as memory usage, inference speed, and finetuning data efficiency. We attempt to fill this gap by performing a comprehensive empirical comparison of multilingual tokenizer-free and subword-based models considering the various dimensions. Surprisingly, we find that subword-based models might still be the most practical choice in many settings, achieving better performance for lower inference latency and memory usage. Based on these results, we encourage future work in tokenizer-free methods to consider these factors when designing and evaluating new models.
Assessing Out-of-Domain Language Model Performance from Few Examples

While pretrained language models have exhibited impressive generalization capabilities, they still behave unpredictably under certain domain shifts. In particular, a model may learn a reasoning process on in-domain training data that does not hold for out-of-domain test data. We address the task of predicting out-of-domain (OOD) performance in a few-shot fashion: given a few target-domain examples and a set of models with similar training performance, can we understand how these models will perform on OOD test data? We benchmark the performance on this task when looking at model accuracy on the few-shot examples, then investigate how to incorporate analysis of the models’ behavior using feature attributions to better tackle this problem. Specifically, we explore a set of factors designed to reveal model agreement with certain pathological heuristics that may indicate worse generalization capabilities. On textual entailment, paraphrase recognition, and a synthetic classification task, we show that attribution-based factors can help rank relative model OOD performance. However, accuracy on a few-shot test set is a surprisingly strong baseline, particularly when the system designer does not have in-depth prior knowledge about the domain shift.
Incorporating Context into Subword Vocabularies

Most current popular subword tokenizers are trained based on word frequency statistics over a corpus, without considering information about co-occurrence or context. Nevertheless, the resulting vocabularies are used in language models’ highly contextualized settings. We present SaGe, a tokenizer that tailors subwords for their downstream use by baking in the contextualized signal at the vocabulary creation phase. We show that SaGe does a better job than current widespread tokenizers in keeping token contexts cohesive, while not incurring a large price in terms of encoding efficiency or domain robustness. SaGe improves performance on English GLUE classification tasks as well as on NER, and on Inference and NER in Turkish, demonstrating its robustness to language properties such as morphological exponence and agglutination.
Shironaam: Bengali News Headline Generation using Auxiliary Information

Automatic headline generation systems have the potential to assist editors in finding interesting headlines to attract visitors or readers. However, the performance of headline generation systems remains challenging due to the unavailability of sufficient parallel data for low-resource languages like Bengali and the lack of ideal approaches to develop a system for headline generation using pre-trained language models, especially for long news articles. To address these challenges, we present Shironaam, a large-scale dataset in Bengali containing over 240K news article-headline pairings with auxiliary data such as image captions, topic words, and category information. Unlike other headline generation models, this paper uses this auxiliary information to better model this task. Furthermore, we utilize the contextualized language models to design encoder-decoder model for Bengali news headline generation and follow a simple yet cost-effective coarse-to-fine approach using topic-words to retrieve important sentences considering the fixed length requirement of the pre-trained language models. Finally, we conduct extensive experiments on our dataset containing news articles of 13 different categories to demonstrate the effectiveness of incorporating auxiliary information and evaluate our system on a wide range of metrics. The experimental results demonstrate that our methods bring significant improvements (i.e., 3 to 10 percentage points across all evaluation metrics) over the baselines. Also to illustrate the utility and robustness, we report experimental results in few-shot and non-few-shot settings.
MMT: A Multilingual and Multi-Topic Indian Social Media Dataset

Social media plays a significant role in cross-cultural communication. A vast amount of this occurs in code-mixed and multilingual form, posing a significant challenge to Natural Language Processing (NLP) tools for processing such information, like language identification, topic modeling, and named-entity recognition. To address this, we introduce a large-scale multilingual and multi-topic dataset MMT collected from Twitter (1.7 million Tweets), encompassing 13 coarse-grained and 63 fine-grained topics in the Indian context. We further annotate a subset of 5,346 tweets from the MMT dataset with various Indian languages and their code-mixed counterparts. Also, we demonstrate that the currently existing tools fail to capture the linguistic diversity in MMT on two downstream tasks, i.e., topic modeling and language identification. To facilitate future research, we will make the anonymized and annotated dataset available in the public domain.
Assessing Information Security Governance in Public Sector Banks of India

Purpose of the study: This study aims to investigate and analyze the Information Security governance practices within banks. Design/methodology/approach: This is a Survey-based study. Employees of State Bank of India in Delhi region were the participants of the study. Findings: The findings of the study will contribute to the existing body of knowledge on information security governance in the banking sector. Research limitations: Small sample size and lack of funds for performing comprehensive quantitative study are the limitations of the study. Practical implications: Regulatory compliance, incident response, and data recovery are all a part of this process, as well as risk assessment and management, policy and procedure creation, security awareness and training, security controls and technology, and more. Public sector organizations may improve their security posture and better secure their information assets by adopting a systematic approach to information security governance. Social implications: Data protection, service protection, privacy, fighting cybercrime, public trust, and economic impact are only few of the societal effects of researching information security governance in public sector organizations. Organizations in the public sector can reduce the risk of financial and reputational damage as well as secure sensitive information by employing best practices in information security. Originality/value: The research outcomes will help identify areas of improvement, highlight effective practices, and provide recommendations for enhancing information security governance within banks. Ultimately, this study contributes to the development of robust Information Security governance frameworks that can protect sensitive data, mitigate risks, ensure regulatory compliance, and maintain the trust and confidence of customers and stakeholders in the banking industry.