Reviewerly/exHarmony huggingface dataset:
Opponent Modeling in Negotiation Dialogues by Related Data Adaptation

Opponent modeling is the task of inferring another party's mental state within the context of social interactions.In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals.A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training.In this work, we propose a ranker for identifying these priorities from negotiation dialogues.The model takes in a partial dialogue as input and predicts the priority order of the opponent.We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent's preferences and offers, as a proxy to relying on granular utterance-level annotations.We show the utility of our proposed approach through extensive experiments based on two dialogue datasets.We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios.Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent.We release our code to support future work in this direction: https://github.com/kushalchawla/opponent-modeling.
R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm

This paper describes our system used for Se-mEval 2022 Task 6: iSarcasmEval: Intended Sarcasm Detection in English and Arabic.We participated in all subtasks based on only English datasets.Pre-trained Language Models (PLMs) have become a de-facto approach for most natural language processing tasks.In our work, we evaluate the performance of these models for identifying sarcasm.For Subtask A and Subtask B, we used simple finetuning on PLMs.For Subtask C, we propose a Siamese network architecture trained using a combination of cross-entropy and distancemaximisation loss.Our model was ranked 7 th in Subtask B, 8 th in Subtask C (English), and performed well in Subtask A (English).In our work, we also present the comparative performance of different PLMs for each Subtask.
KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition

In this work, we introduce our system to the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) competition.Our team (KDDIE) attempted the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results.For this task, we use transfer learning method: fine-tuning the pre-trained language models (PLMs) on the competition dataset.Our two approaches are the BERTbased PLMs and PLMs with additional layer such as Condition Random Field.We report our finding and results in this report.
Word-level Morpheme segmentation using Transformer neural network

This paper presents the submission of team NUM DI to the SIGMORPHON 2022 Task on Morpheme Segmentation Part 1, word-level morpheme segmentation.We explore the transformer neural network approach to the shared task.We develop monolingual models for world-level morpheme segmentation and focus on improving the model by using various training strategies to improve accuracy and generalization across languages.
DUCS at SemEval-2022 Task 6: Exploring Emojis and Sentiments for Sarcasm Detection

This paper describes the participation of team DUCS at SemEval 2022 Task 6: iSarcasmEval -Intended Sarcasm Detection in English and Arabic.Team DUCS participated in SubTask A of iSarcasmEval which was to determine if the given English text was sarcastic or not.In this work, emojis were utilized to capture how they contributed to the sarcastic nature of a text.It is observed that emojis can augment or reverse the polarity of a given statement.Thus sentiment polarities and intensities of emojis, as well as those of text, were computed to determine sarcasm.Use of capitalization, word repetition, and use of punctuation marks like '!' were factored in as sentiment intensifiers.An NLP augmenter was used to tackle the imbalanced nature of the sarcasm dataset.Several architectures comprising of various ML and DL classifiers, and transformer models like BERT and Multimodal BERT were experimented with.It was observed that Multimodal BERT outperformed other architectures tested and achieved an F1-score of 30.71%.The key takeaway of this study was that sarcastic texts are usually positive sentences.In general emojis with positive polarity are used more than those with negative polarities in sarcastic texts.
drsphelps at SemEval-2022 Task 2: Learning idiom representations using BERTRAM

This paper describes our system for SemEval-2022 Task 2 Multilingual Idiomaticity Detection and Sentence Embedding sub-task B. We modify a standard BERT sentence transformer by adding embeddings for each idioms, which are created using BERTRAM and a small number of contexts.We show that this technique increases the quality of idiom representations and leads to better performance on the task.We also perform analysis on our final results and show that the quality of the produced idiom embeddings is highly sensitive to the quality of the input contexts.
Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfer

Recent studies show that auto-encoder based approaches successfully perform language generation, smooth sentence interpolation, and style transfer over unseen attributes using unlabelled datasets in a zero-shot manner.The latent space geometry of such models is organised well enough to perform on datasets where the style is "coarse-grained" i.e. a small fraction of words alone in a sentence are enough to determine the overall style label.A recent study uses a discrete token-based perturbation approach to map "similar" sentences ("similar" defined by low Levenshtein distance/ high word overlap) close by in latent space.This definition of "similarity" does not look into the underlying nuances of the constituent words while mapping latent space neighbourhoods and therefore fails to recognise sentences with different style-based semantics while mapping latent neighbourhoods.We introduce EPAAEs (Embedding Perturbed Adversarial AutoEncoders) which completes this perturbation model, by adding a finely adjustable noise component on the continuous embeddings space.We empirically show that this (a) produces a better organised latent space that clusters stylistically similar sentences together, (b) performs best on a diverse set of text style transfer tasks than similar denoising-inspired baselines, and (c) is capable of fine-grained control of Style Transfer strength.We also extend the text style transfer tasks to NLI datasets and show that these more complex definitions of style are learned best by EPAAE.To the best of our knowledge, extending style transfer to NLI tasks has not been explored before.
Mask and Regenerate: A Classifier-based Approach for Unpaired Sentiment Transformation of Reviews for Electronic Commerce Websites.

Style transfer is the task of transferring a sentence into the target style while keeping its content. The major challenge is that parallel corpora are not available for various domains. In this paper, we propose a Mask-And-Regenerate approach (MAR). It learns from unpaired sentences by modifying the word-level style attributes. We cautiously integrate the deletion, insertion and substitution operations into our model. This enables our model to automatically apply different edit operations for different sentences. Specifically, we train a multilayer perceptron (MLP) as a style classifier to find out and mask style-characteristic words in the source inputs. Then we learn a language model on non-parallel data sets to score sentences and remove unnecessary masks. Finally, the masked source sentences are input to a Transformer to perform style transfer. The final results show that our proposed model exceeds baselines by about 2 per cent of accuracy for both sentiment and style transfer tasks with comparable or better content retention.
On Gender Biases in Offensive Language Classification Models

We explore whether neural Natural Language Processing models trained to identify offensive language in tweets contain gender biases.We add historically gendered and gender ambiguous American names to an existing offensive language evaluation set to determine whether models' predictions are sensitive or robust to gendered names.While we see some evidence that these models might be prone to biased stereotypes that men use more offensive language than women, our results indicate that these models' binary predictions might not greatly change based upon gendered names.
Partial-input baselines show that NLI models can ignore context, but they don’t.

When strong partial-input baselines reveal artifacts in crowdsourced NLI datasets, the performance of full-input models trained on such datasets is often dismissed as reliance on spurious correlations.We investigate whether stateof-the-art NLI models are capable of overriding default inferences made by a partial-input baseline.We introduce an evaluation set of 600 examples consisting of perturbed premises to examine a RoBERTa model's sensitivity to edited contexts.Our results indicate that NLI models are still capable of learning to condition on context-a necessary component of inferential reasoning-despite being trained on artifact-ridden datasets.
Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders

Abstractive summarization can generate high quality results with the development of the neural network.However, generating factual consistency summaries is a challenging task for abstractive summarization.Recent studies extract the additional information with off-the-shelf tools from the source document as a clue to guide the summary generation, which shows effectiveness to improve the faithfulness.Unlike these work, we present a novel framework based on conditional variational autoencoders, which induces the guidance information and generates the summary equipped with the guidance synchronously.Experiments on XSUM and CNNDM dataset show that our approach can generate relevant and fluent summaries which is more faithful than the existing state-of-theart approaches, according to multiple factual consistency metrics.
RRED : A Radiology Report Error Detector based on Deep Learning Framework

Radiology report is an official record of radiologists' interpretation of patients' radiographs and it's a crucial component in the overall medical diagnostic process.However, it can contain various types of errors that can lead to inadequate treatment or delay in diagnosis.To address this problem, we propose a deep learning framework to detect errors in radiology reports.Specifically, our method detects errors between findings and conclusion of chest X-ray reports based on a supervised learning framework.To compensate for the lack of data availability of radiology reports with errors, we develop an error generator to systematically create artificial errors in existing reports.In addition, we introduce a Medical Knowledge-enhancing Pretraining to further utilize the knowledge of abbreviations and key phrases frequently used in the medical domain.We believe that this is the first work to propose a deep learning framework for detecting errors in radiology reports based on a rich contextual and medical understanding.Validation on our radiologistsynthesized dataset, based on MIMIC-CXR, shows 0.80 and 0.95 of the area under precisionrecall curve (AUPRC) and the area under the ROC curve (AUROC) respectively, indicating that our framework can effectively detect errors in the real-world radiology reports.
PINGAN Omini-Sinitic at SemEval-2022 Task 4: Multi-prompt Training for Patronizing and Condescending Language Detection

This paper describes the second-placed system for subtask 2 and the ninth-placed system for subtask 1 in SemEval 2022 Task 4: Patronizing and Condescending Language Detection.We propose an ensemble of prompt training and label attention mechanism for multi-label classification tasks.Transfer learning is introduced to transfer the knowledge from binary classification to multi-label classification.The experimental results proved the effectiveness of our proposed method.The ablation study is also conducted to show the validity of each technique.
GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type Identification

Sarcasm refers to the use of words that have different literal and intended meanings.It represents the usage of words that are opposite of what is literally said, especially in order to insult, mock, criticise or irritate someone.These types of statements may be funny or amusing to others but may hurt or annoy the person towards whom it is intended.Identification of sarcastic phrases from social media posts finds its application in different domains like sentiment analysis, opinion mining, author profiling and harassment detection.We have proposed a model for the shared task iSarcasmEval -Intended Sarcasm Detection in English and Arabic by SemEval-2022 considering the language English.The Subtask A and Subtask C were implemented using a Convolutional Neural Network based classifier which makes use of ELMo embeddings.The Subtask B was implemented using Gaussian Naive Bayes classifier by extracting TF-IDF vectors.The proposed models resulted in macro-F1 scores of 0.2012, 0.0387 and 0.2794 for sarcastic texts in Subtasks A, B and C respectively.
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation.

Stemming from the limited availability of datasets and textual resources for low-resource languages such as isiZulu, there is a significant need to be able to harness knowledge from pre-trained models to improve low resource machine translation.Moreover, a lack of techniques to handle the complexities of morphologically rich languages has compounded the unequal development of translation models, with many widely spoken African languages being left behind.This study explores the potential benefits of transfer learning in an English-isiZulu translation framework.The results indicate the value of transfer learning from closely related languages to enhance the performance of low-resource translation models, thus providing a key strategy for low-resource translation going forward.We gathered results from 8 different language corpora, including one multilingual corpus, and saw that isiXhosa-isiZulu outperformed all languages, with a BLEU score of 8.56 on the test set which was better from the multi-lingual corpora pre-trained model by 2.73.We also derived a new coefficient, Nasir's Geographical Distance Coefficient (NGDC) which provides an easy selection of languages for the pre-trained models.NGDC also indicated that isiXhosa should be selected as the language for the pre-trained model.
The Birth of Bias: A case study on the evolution of gender bias in an English language model

Detecting and mitigating harmful biases in modern language models are widely recognized as crucial, open problems. In this paper, we take a step back and investigate how language models come to be biased in the first place.We use a relatively small language model, using the LSTM architecture trained on an English Wikipedia corpus. With full access to the data and to the model parameters as they change during every step while training, we can map in detail how the representation of gender develops, what patterns in the dataset drive this, and how the model’s internal state relates to the bias in a downstream task (semantic textual similarity).We find that the representation of gender is dynamic and identify different phases during training.Furthermore, we show that gender information is represented increasingly locally in the input embeddings of the model and that, as a consequence, debiasing these can be effective in reducing the downstream bias.Monitoring the training dynamics, allows us to detect an asymmetry in how the female and male gender are represented in the input embeddings. This is important, as it may cause naive mitigation strategies to introduce new undesirable biases.We discuss the relevance of the findings for mitigation strategies more generally and the prospects of generalizing our methods to larger language models, the Transformer architecture, other languages and other undesirable biases.
A Computational Acquisition Model for Multimodal Word Categorization

Recent advances in self-supervised modeling of text and images open new opportunities for computational models of child language acquisition, which is believed to rely heavily on cross-modal signals.However, prior studies have been limited by their reliance on vision models trained on large image datasets annotated with a pre-defined set of depicted object categories.This is (a) not faithful to the information children receive and (b) prohibits the evaluation of such models with respect to category learning tasks, due to the pre-imposed category structure.We address this gap, and present a cognitively-inspired, multimodal acquisition model, trained from image-caption pairs on naturalistic data using cross-modal self-supervision.We show that the model learns word categories and object recognition abilities, and presents trends reminiscent of those reported in the developmental literature.We make our code and trained models public for future reference and use 1 .
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning

Automatic Speech Recognition (ASR) systems typically produce unpunctuated transcripts that have poor readability.In addition, building a punctuation restoration system is challenging for low-resource languages, especially for domain-specific applications.In this paper, we propose a Spanish punctuation restoration system designed for a real-time customer support transcription service.To address the data sparsity of Spanish transcripts in the customer support domain, we introduce two transferlearning-based strategies: 1) domain adaptation using out-of-domain Spanish text data; 2) crosslingual transfer learning leveraging in-domain English transcript data.Our experiment results show that these strategies improve the accuracy of the Spanish punctuation restoration system.
ULFRI at SemEval-2022 Task 4: Leveraging uncertainty and additional knowledge for patronizing and condescending language detection

We describe the ULFRI system used in the Subtask 1 of SemEval-2022 Task 4 Patronizing and condescending language detection.Our models are based on the RoBERTa model, modified in two ways: (1) by injecting additional knowledge (coreferences, named entities, dependency relations, and sentiment) and ( 2) by leveraging the task uncertainty by using soft labels, Monte Carlo dropout, and threshold optimization.We find that the injection of additional knowledge is not helpful but the uncertainty management mechanisms lead to small but consistent improvements.Our final system based on these findings achieves F 1 = 0.575 in the online evaluation, ranking 19th out of 78 systems.
CoSe-Co: Text Conditioned Generative CommonSense Contextualizer

Pre-trained Language Models (PTLMs) have been shown to perform well on natural language tasks.Many prior works have leveraged structured commonsense present in the form of entities linked through labeled relations in Knowledge Graphs (KGs) to assist PTLMs.Retrieval approaches use KG as a separate static module which limits coverage since KGs contain finite knowledge.Generative methods train PTLMs on KG triples to improve the scale at which knowledge can be obtained.However, training on symbolic KG entities limits their applicability in tasks involving natural language text where they ignore overall context.To mitigate this, we propose a CommonSense Contextualizer (CoSe-Co) conditioned on sentences as input to make it generically usable in tasks for generating knowledge relevant to the overall context of input text.To train CoSe-Co, we propose a novel dataset comprising of sentence and commonsense knowledge pairs.The knowledge inferred by CoSe-Co is diverse and contain novel entities not present in the underlying KG.We augment generated knowledge in Multi-Choice QA and Open-ended CommonSense Reasoning tasks leading to improvements over current best methods on CSQA, ARC, QASC and OBQA datasets.We also demonstrate its applicability in improving performance of a baseline model for paraphrase generation task.
A Comparative Study on Word Embeddings and Social NLP Tasks

In recent years, grey social media platforms, those with a loose moderation policy on cyberbullying, have been attracting more users.Recently, data collected from these types of platforms have been used to pre-train word embeddings (social-media-based), yet these word embeddings have not been investigated for social NLP related tasks.In this paper, we carried out a comparative study between social-mediabased and non-social-media-based word embeddings on two social NLP tasks: Detecting cyberbullying and Measuring social bias.Our results show that using social-media-based word embeddings as input features, rather than non-social-media-based embeddings, leads to better cyberbullying detection performance.We also show that some word embeddings are more useful than others for categorizing offensive words.However, we do not find strong evidence that certain word embeddings will necessarily work best when identifying certain categories of cyberbullying within our datasets.Finally, We show even though most of the state-of-the-art bias metrics ranked social-media-based word embeddings as the most socially biased, these results remain inconclusive and further research is required.
End-to-End Chinese Speaker Identification

Speaker identification (SI) in texts aims to identify the speaker(s) for each utterance in texts.Previous studies divide SI into several sub-tasks (e.g., quote extraction, named entity recognition, gender identification, and coreference resolution).However, we are still far from solving these sub-tasks, making SI systems that rely on them seriously suffer from error propagation.End-to-end SI systems, on the other hand, are not limited by individual modules, but suffer from insufficient training data from the existing small-scale datasets.To make large end-to-end models possible, we design a new annotation guideline that regards SI as span extraction from the local context, and we annotate by far the largest SI dataset for Chinese named CSI based on eighteen novels.Viewing SI as a span extraction task also introduces the possibility of applying existing storng extractive machine reading comprehension (MRC) baselines.Surprisingly, simply using such a baseline without human-annotated character names and carefully designed rules, we can already achieve performance comparable or better than those of previous state-of-the-art SI methods on all public SI datasets for Chinese.Furthermore, we show that our dataset can serve as additional training data for existing benchmarks, which leads to further gains (up to 6.5% in accuracy).Finally, using CSI as a clean source, we design an effective self-training paradigm to continuously leverage hundreds of unlabeled novels.