Reviewerly/exHarmony huggingface dataset:
Monolingual Phrase Alignment as Parse Forest Mapping

We tackle the problem of monolingual phrase alignment conforming to syntactic structures.The existing method formalises the problem as unordered tree mapping; hence, the alignment quality is easily affected by syntactic ambiguities.We address this problem by expanding the method to align parse forests rather than 1-best trees, where syntactic structures and phrase alignment are simultaneously identified.The proposed method achieves efficient alignment by mapping forests on a packed structure.The experimental results indicated that our method improves the phrase alignment quality of the state-of-the-art method by aligning forests rather than 1-best trees.
Query Generation Using GPT-3 for CLIP-Based Word Sense Disambiguation for Image Retrieval

In this study, we propose using the GPT-3 as a query generator for the backend of CLIP as an implicit word sense disambiguation (WSD) component for the SemEval 2023 shared task Visual Word Sense Disambiguation (VWSD).We confirmed previous findings -human-like prompts adapted for WSD with quotes benefit both CLIP and GPT-3, whereas plain phrases or poorly templated prompts yield the worst results.Our code is available at https:// github.com/pxm427/WSD-for-IR.
Leveraging Active Learning to Minimise SRL Annotation Across Corpora

In this paper we investigate the application of active learning to semantic role labeling (SRL) using Bayesian Active Learning by Disagreement (BALD). Our new predicate-focused selection method quickly improves efficiency on three different specialised domain corpora. This is encouraging news for researchers wanting to port SRL to domain specific applications. Interestingly, with the large and diverse \textit{OntoNotes} corpus, the sentence selection approach, that collects a larger number of predicates, taking more time to annotate, fares better than the predicate approach. In this paper, we analyze both the selections made by our two selections methods for the various domains and the differences between these corpora in detail.
Modelling Political Aggression on Social Media Platforms

Recent years have seen a proliferation of aggressive social media posts, often wreaking even real-world consequences for victims.Aggressive behaviour on social media is especially evident during important sociopolitical events such as elections, communal incidents, and public protests.In this paper, we introduce a dataset in English to model political aggression 1 .The dataset comprises public tweets collated across the time-frames of two of the most recent Indian general elections.We manually annotate this data for the task of aggression detection and analyze this data for aggressive behaviour.To benchmark the efficacy of our dataset, we perform experiments by fine-tuning pre-trained language models and comparing the results with models trained on an existing but general domain dataset.Our models consistently outperform the models trained on existing data.Our best model achieves a macro F1-score of 66.66 on our dataset.We also train models on a combined version of both datasets, achieving the best macro F1-score of 92.77, on our dataset.Additionally, we create subsets of code-mixed and non-code-mixed data from the combined dataset to observe variations in results due to the Hindi-English code-mixing phenomenon.We publicly release the anonymized data, code, and models for further research.
Team Bias Busters at WASSA 2023 Empathy, Emotion and Personality Shared Task: Emotion Detection with Generative Pretrained Transformers

This paper describes the approach that we used to take part in the multi-label multi-class emotion classification as Track 3 of the WASSA 2023 Empathy, Emotion and Personality Shared Task at ACL 2023. The overall goal of this track is to build models that can predict 8 classes (7 emotions + neutral) based on short English essays written in response to news article that talked about events perceived as harmful to people. We used OpenAI generative pretrained transformers with full-scale APIs for the emotion prediction task by fine-tuning a GPT-3 model and doing prompt engineering for zero-shot / few-shot learning with ChatGPT and GPT-4 models based on multiple experiments on the dev set. The most efficient method was fine-tuning a GPT-3 model which allowed us to beat our baseline character-based XGBoost Classifier and rank 2nd among all other participants by achieving a macro F1 score of 0.65 and a micro F1 score of 0.7 on the final blind test set.
Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis

Counterhate arguments can effectively fight and limit the spread of hate speech.However, they can also exacerbate the hate, as some people may respond with aggression if they feel threatened or targeted by the counterhate.In this paper, we investigate replies to counterhate arguments beyond whether the reply agrees or disagrees with the counterhate argument.We present a corpus with 2,621 replies to counterhate arguments countering hateful tweets, and annotate them with fine-grained characteristics.We show that (a) half of the replies (51%) to the counterhate arguments disagree with the argument, and (b) this kind of reply often supports the hateful tweet (40%).We also analyze the language of counterhate arguments that elicit certain types of replies.Experimental results show that it is feasible to anticipate the kind of replies a counterhate argument will elicit.Hateful Tweet 1: I f**king hate <ethnicity> people.[...] I hope you all f
SigMoreFun Submission to the SIGMORPHON Shared Task on Interlinear Glossing

In our submission to the SIGMORPHON 2023 Shared Task on interlinear glossing (IGT), we explore approaches to data augmentation and modeling across seven low-resource languages.For data augmentation, we explore two approaches: creating artificial data from the provided training data and utilizing existing IGT resources in other languages.On the modeling side, we test an enhanced version of the provided token classification baseline as well as a pretrained multilingual seq2seq model.Additionally, we apply post-correction using a dictionary for Gitksan, the language with the smallest amount of data.We find that our token classification models are the best performing, with the highest word-level accuracy for Arapaho and highest morpheme-level accuracy for Gitksan out of all submissions.We also show that data augmentation is an effective strategy, though applying artificial data pretraining has very different effects across both models tested.
Emotion classification on code-mixed text messages via soft prompt tuning

Emotion classification on code-mixed text messages is challenging due to the multilingual languages and non-literal cues (i.e., emoticons).To solve these problems, we propose an innovative soft prompt tuning method, which is lightweight and effective to release potential abilities of the pre-trained language models and improve the classification results.Firstly, we transform emoticons into textual information to utilize their rich emotional information.Then, variety of innovative templates and verbalizers are applied to promote emotion classification.Extensive experiments show that transforming emoticons and employing prompt tuning both benefit the performance.Finally, as a part of WASSA 2023, we obtain the accuracy of 0.972 in track MLEC and 0.892 in track MCEC, yielding the second place in both two tracks.
Adapting Emotion Detection to Analyze Influence Campaigns on Social Media

Social media is an extremely potent tool for influencing public opinion, particularly during important events such as elections, pandemics, and national conflicts.Emotions are a crucial aspect of this influence, but detecting them accurately in the political domain is a significant challenge due to the lack of suitable emotion labels and training datasets.In this paper, we present a generalized approach to emotion detection that can be adapted to the political domain with minimal performance sacrifice.Our approach is designed to be easily integrated into existing models without the need for additional training or fine-tuning.We demonstrate the zero-shot and few-shot performance of our model on the 2017 French presidential elections and propose efficient emotion groupings that would aid in effectively analyzing influence campaigns and agendas on social media.
SIGMORPHON–UniMorph 2023 Shared Task 0, Part 2: Cognitively Plausible Morphophonological Generalization in Korean

This paper summarises data collection and curation for Part 2 of the 2023 SIGMORPHON-UniMorph Shared Task 0, which focused on modeling speaker knowledge and generalization of a pair of interacting phonological processes in Korean.We briefly describe how modeling the generalization task could be of interest to researchers in both Natural Language Processing and linguistics, and then summarise the traditional description of the phonological processes that are at the center of the modeling challenge.We then describe the criteria we used to select and code cases of process application in two Korean speech corpora, which served as the primary learning data.We also report the technical details of the experiment we carried out that served as the primary test data. 1
Low-resource grapheme-to-phoneme mapping with phonetically-conditioned transfer

In this paper we explore a very simple nonneural approach to mapping orthography to phonetic transcription in a low-resource context with transfer data from a related language.We start from a baseline system and focus our efforts on data augmentation.We make three principal moves.First, we start with an HMMbased system (Novak et al., 2012).Second, we augment our basic system by recombining legal substrings in restricted fashion (Ryan and Hulden, 2020).Finally, we limit our transfer data by only using training pairs where the phonetic form shares all bigrams with the target language.
JSEEGraph: Joint Structured Event Extraction as Graph Parsing

We propose a graph-based event extraction framework JSEEGraph that approaches the task of event extraction as general graph parsing in the tradition of Meaning Representation Parsing.It explicitly encodes entities and events in a single semantic graph, and further has the flexibility to encode a wider range of additional IE relations and jointly infer individual tasks.JSEEGraph performs in an end-to-end manner via general graph parsing: (1) instead of flat sequence labelling, nested structures between entities/triggers are efficiently encoded as separate nodes in the graph, allowing for nested and overlapping entities and triggers; (2) both entities, relations, and events can be encoded in the same graph, where entities and event triggers are represented as nodes and entity relations and event arguments are constructed via edges;(3) joint inference avoids error propagation and enhances the interpolation of different IE tasks.We experiment on two benchmark datasets of varying structural complexities; ACE05 and Rich ERE, covering three languages: English, Chinese, and Spanish.Experimental results show that JSEEGraph can handle nested event structures, that it is beneficial to solve different IE tasks jointly, and that event argument extraction in particular benefits from entity extraction.Our code and models are released as open-source 1 .1 https://github.com/huiling-y/JSEEGraph , purposely trigger
Can ChatGPT Understand Causal Language in Science Claims?

This study evaluated ChatGPT's ability to understand causal language in science papers and news by testing its accuracy in a task of labeling the strength of a claim as causal, conditional causal, correlational, or no relationship.The results show that ChatGPT is still behind the existing fine-tuned BERT models by a large margin.ChatGPT also had difficulty understanding conditional causal claims mitigated by hedges.However, its weakness may be utilized to improve the clarity of human annotation guideline.Chain-of-thought prompting was faithful and helpful for improving prompt performance, but finding the optimal prompt is difficult with inconsistent results and the lack of effective method to establish cause-effect between prompts and outcomes, suggesting caution when generalizing prompt engineering results across tasks or models.
SIGMORPHON 2022 Shared Task on Grapheme-to-Phoneme Conversion Submission Description: Sequence Labelling for G2P

This paper describes our participation in the Third SIGMORPHON Shared Task on Grapheme-to-Phoneme Conversion (Low-Resource and Cross-Lingual) (McCarthy et al., 2022).Our models rely on different sequence labelling methods.The main model predicts multiple phonemes from each grapheme and is trained using CTC loss (Graves et al., 2006).We find that sequence labelling methods yield worse performance than the baseline when enough data is available, but can still be used when very little data is available.Furthermore, we demonstrate that alignments learned by the sequence labelling models can be easily inspected.
PENGARUH PENGGUNAAN MEDIA PEMBELAJARAN BERBASIS CANVA TERHADAP HASIL BELAJAR PKN SISWA KELAS 3 SD NEGERI 2 KAYU AGUNG

This study aims to determine the effect of using Canva-based learning media on PKN learning outcomes for class III students at SD Negeri 2 Kayu Agung. The research method used in this study was a Quasi-Experimental Posttest with a sample of 55 people. The sample in this study was taken using a simple random sampling technique. The data collection technique used was a multiple choice test with 20 questions. Based on the results of the study it can be seen that the significance value of 0 = 0.006 is smaller than alpha (α) = 0.05; with a tcount value of 3.226 and a ttable value of 1.672. It turns out that tcount &gt; ttable or 2.889 &gt; 1.674. This means that there is the effectiveness of Canva-based learning media on the learning outcomes of Civics in class III SD Negeri 2 Kayu Agung.&#x0D; Keywords: canva-based media, learning outcomes
The BGU-MeLeL System for the SIGMORPHON 2023 Shared Task on Morphological Inflection

This paper presents the submission by the MeLeL team to the SIGMORPHON–UniMorph Shared Task on Typologically Diverse and Acquisition-Inspired Morphological Inflection Generation Part 3: Models of Acquisition of Inflectional Noun Morphology in Polish, Estonian, and Finnish. This task requires us to produce the word form given a lemma and a grammatical case, while trying to produce the same error-rate as in children. We approach this task with a reduced-size character-based transformer model, multilingual training and an upsampling method to introduce bias.
A future for universal grapheme-phoneme transduction modeling with neuralized finite-state transducers

We propose a universal grapheme-phoneme transduction model using neuralized finite-state transducers.
Improving Automated Prediction of English Lexical Blends Through the Use of Observable Linguistic Features

The process of lexical blending is difficult to reliably predict. This difficulty has been shown by machine learning approaches in blend modeling, including attempts using then state-of-the-art LSTM deep neural networks trained on character embeddings, which were able to predict lexical blends given the ordered constituent words in less than half of cases, at maximum. This project introduces a novel model architecture which dramatically increases the correct prediction rates for lexical blends, using only Polynomial regression and Random Forest models. This is achieved by generating multiple possible blend candidates for each input word pairing and evaluating them based on observable linguistic features. The success of this model architecture illustrates the potential usefulness of observable linguistic features for problems that elude more advanced models which utilize only features discovered in the latent space.
Exploration of Contrastive Learning Strategies toward more Robust Stance Detection

Stance Detection is the task of identifying the position of an author of a text towards an issue or a target.Previous studies on Stance Detection indicate that the existing systems are non-robust to the variations and errors in input sentences.Our proposed methodology uses Contrastive Learning to learn sentence representations by bringing semantically similar sentences and sentences implying the same stance closer to each other in the embedding space.We compare our approach to a pretrained transformer model directly finetuned with the stance datasets.We use char-level and word-level adversarial perturbation attacks to measure the resilience of the models and we show that our approach achieves better performances and is more robust to the different adversarial perturbations introduced to the test data.The results indicate that our approach performs better on small-sized and class-imbalanced stance datasets.
SIGMORPHON–UniMorph 2023 Shared Task 0: Typologically Diverse Morphological Inflection

The 2023 SIGMORPHON-UniMorph shared task on typologically diverse morphological inflection included a wide range of languages: 26 languages from 9 primary language families.The data this year was all lemma-split, to allow testing models' generalization ability, and structured along the new hierarchical schema presented in (Batsuren et al., 2022).The systems submitted this year, 9 in number, showed ingenuity and innovativeness, including hard attention for explainability and bidirectional decoding.Special treatment was also given by many participants to the newly-introduced data in Japanese, due to the high abundance of unseen Kanji characters in its test set. 1
Morphological reinflection with weighted finite-state transducers

This paper describes the submission by the University of Arizona to the SIGMORPHON 2023 Shared Task on typologically diverse morphological (re-)infection.In our submission, we investigate the role of frequency, length, and weighted transducers in addressing the challenge of morphological reinflection.We start with the non-neural baseline provided for the task and show how some improvement can be gained by integrating length and frequency in prefix selection.We also investigate using weighted finite-state transducers, jump-started from edit distance and directly augmented with frequency.Our specific technique is promising and quite simple, but we see only modest improvements for some languages here.
Evaluating Cross Lingual Transfer for Morphological Analysis: a Case Study of Indian Languages

Recent advances in pretrained multilingual models such as Multilingual T5 (mT5) have facilitated cross-lingual transfer by learning shared representations across languages.Leveraging pre-trained multilingual models for scaling morphology analyzers to low-resource languages is a unique opportunity that has been under-explored so far.We investigate this line of research in the context of Indian languages, focusing on two important morphological subtasks: root word extraction and tagging morphosyntactic descriptions (MSD), viz., gender, number, and person (GNP).We experiment with six Indian languages from two language families (Dravidian and Indo-Aryan) to train a multilingual morphology analyzers for the first time for Indian languages.We demonstrate the usability of multilingual models for fewshot cross-lingual transfer through an average 7% increase in GNP tagging in a cross-lingual setting as compared to a monolingual setting through controlled experiments.We provide an overview of the state of the datasets available related to our tasks and point-out a few modeling limitations due to datasets.Lastly, we analyze the cross-lingual transfer of morphological tags for verbs and nouns, which provides a proxy for the quality of representations of word markings learned by the model.
Glossy Bytes: Neural Glossing using Subword Encoding

This paper presents several subword-modellingbased approaches to interlinear glossing for seven under-resourced languages as a part of the 2023 SIGMORPHON shared task on interlinear glossing (Ginn et al., 2023).In an interlinear glossed text (IGT), each line of the original text is paired with one or more corresponding lines which encode the underlying grammatical structure.While expert annotated glossed text is especially valuable for the study of low-resource languages in both theoretical linguistics and natural language processing, generating high-quality glossed data is expensive and time-consuming.Therefore, approaches which aim to automatically or semiautomatically generate glossed data can be valuable for linguistic research.We experiment with various augmentation and tokenization strategies for both the open and closed tracks of data.We found that while subword models may perform well for greater amounts of data, character-based approaches remain competitive in their performance in lower resource settings.* *The first two authors contributed equally. 1 Throughout this paper, we use character to refer to simple character-level splitting and subword to refer to all other subword segmentation
Lightweight morpheme labeling in context: Using structured linguistic representations to support linguistic analysis for the language documentation context

Linguistic analysis is a core task in the process of documenting, analyzing, and describing endangered and less-studied languages.In addition to providing insight into the properties of the language being studied, having tools to automatically label words in a language for grammatical category and morphological features can support a range of applications useful for language pedagogy and revitalization.At the same time, most modern NLP methods for these tasks require both large amounts of data in the language and compute costs well beyond the capacity of most research groups and language communities.In this paper, we present a gloss-to-gloss (g2g) model for linguistic analysis (specifically, morphological analysis and part-of-speech tagging) that is lightweight in terms of both data requirements and computational expense.The model is designed for the interlinear glossed text (IGT) format, in which we expect the source text of a sentence in a low-resource language, a translation of that sentence into a language of wider communication, and a detailed glossing of the morphological properties of each word in the sentence.We first produce silver standard parallel glossed data by automatically labeling the high-resource translation.The model then learns to transform source language morphological labels into output labels for the target language, mediated by a structured linguistic representation layer.We test the model on both low-resource and high-resource languages, and find that our simple CNN-based model achieves comparable performance to a state-of-the-art transformer-based model, at a fraction of the computational cost.
Jambu: A historical linguistic database for South Asian languages

We introduce JAMBU, a cognate database of South Asian languages which unifies dozens of previous sources in a structured and accessible format.The database includes 287k lemmata from 602 lects, grouped together in 23k sets of cognates.We outline the data wrangling necessary to compile the dataset and train neural models for reflex prediction on the Indo-Aryan subset of the data.We hope that JAMBU is an invaluable resource for all historical linguists and Indologists, and look towards further improvement and expansion of the database. 1