pwc-archive/papers-with-abstracts huggingface dataset:
OSU at SigMorphon 2022: Analogical Inflection With Rule Features
Authors: Micha Elsner, Sara Court
Date: 2022-07-01 00:00:00
Proceeding: NAACL (SIGMORPHON) 2022 7

OSU’s inflection system is a transformer whose input is augmented with an analogical exemplar showing how to inflect a different word into the target cell. In addition, alignment-based heuristic features indicate how well the exemplar is likely to match the output. OSU’s scores substantially improve over the baseline transformer for instances where an exemplar is available, though not quite matching the challenge winner. In Part 2, the system shows a tendency to over-apply the majority pattern in English, but not Arabic.
Generalizing Morphological Inflection Systems to Unseen Lemmas
Authors: Changbing Yang, Ruixin (Ray) Yang, Garrett Nicolai, Miikka Silfverberg
Date: 2022-07-01 00:00:00
Proceeding: NAACL (SIGMORPHON) 2022 7
Tasks: Hallucination, LEMMA, Morphological Inflection

This paper presents experiments on morphological inflection using data from the SIGMORPHON-UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection. We present a transformer inflection system, which enriches the standard transformer architecture with reverse positional encoding and type embeddings. We further apply data hallucination and lemma copying to augment training data. We train models using a two-stage procedure: (1) We first train on the augmented training data using standard backpropagation and teacher forcing. (2) We then continue training with a variant of the scheduled sampling algorithm dubbed student forcing. Our system delivers competitive performance under the small and large data conditions on the shared task datasets.
HeiMorph at SIGMORPHON 2022 Shared Task on Morphological Acquisition Trajectories
Authors: Akhilesh Kakolu Ramarao, Yulia Zinova, Kevin Tang, Ruben van de Vijver
Date: 2022-07-01 00:00:00
Proceeding: NAACL (SIGMORPHON) 2022 7
Tasks: Hallucination, Task 2

This paper presents the submission by the HeiMorph team to the SIGMORPHON 2022 task 2 of Morphological Acquisition Trajectories. Across all experimental conditions, we have found no evidence for the so-called Ushaped development trajectory. Our submitted systems achieve an average test accuracies of 55.5% on Arabic, 67% on German and 73.38% on English. We found that, bigram hallucination provides better inferences only for English and Arabic and only when the number of hallucinations remains low.
Morphology is not just a naive Bayes – UniMelb Submission to SIGMORPHON 2022 ST on Morphological Inflection
Authors: Andreas Sherbakov, Ekaterina Vylomova
Date: 2022-07-01 00:00:00
Proceeding: NAACL (SIGMORPHON) 2022 7
Tasks: LEMMA, Morphological Inflection

The paper describes the Flexica team’s submission to the SIGMORPHON 2022 Shared Task 1 Part 1: Typologically Diverse Morphological Inflection. Our team submitted a nonneural system that extracted transformation patterns from alignments between a lemma and inflected forms. For each inflection category, we chose a pattern based on its abstractness score. The system outperformed the non-neural baseline, the extracted patterns covered a substantial part of possible inflections. However, we discovered that such score that does not account for all possible combinations of string segments as well as morphosyntactic features is not sufficient for a certain proportion of inflection cases.
Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings
Authors: Timothee Mickus, Kees Van Deemter, Mathieu Constant, Denis Paperno
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Word Embeddings

Word embeddings have advanced the state of the art in NLP across numerous tasks. Understanding the contents of dense neural representations is of utmost interest to the computational semantics community. We propose to focus on relating these opaque word vectors with human-readable definitions, as found in dictionaries This problem naturally divides into two subtasks: converting definitions into embeddings, and converting embeddings into definitions. This task was conducted in a multilingual setting, using comparable sets of embeddings trained homogeneously.
LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse Dictionary
Authors: Bin Li, Yixuan Weng, Fei Xia, Shizhu He, Bin Sun, Shutao Li
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Reverse Dictionary, Word Embeddings

This paper introduces the approach of Team LingJing’s experiments on SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings (CODWOE). This task aims at comparing two types of semantic descriptions and including two sub-tasks: the definition modeling and reverse dictionary track. Our team focuses on the reverse dictionary track and adopts the multi-task self-supervised pre-training for multilingual reverse dictionaries. Specifically, the randomly initialized mDeBERTa-base model is used to perform multi-task pre-training on the multilingual training datasets. The pre-training step is divided into two stages, namely the MLM pre-training stage and the contrastive pre-training stage. The experimental results show that the proposed method has achieved good performance in the reverse dictionary track, where we rank the 1-st in the Sgns targets of the EN and RU languages. All the experimental codes are open-sourced at https://github.com/WENGSYX/Semeval.
TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations
Authors: Aditya Srivastava, Harsha Vardhan Vemulapati
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Contrastive Learning, Representation Learning, Reverse Dictionary, Word Embeddings

We propose a pair of deep learning models, which employ unsupervised pretraining, attention mechanisms and contrastive learning for representation learning from dictionary definitions, and definition modeling from such representations. Our systems, the Transformers for Learning Dictionaries and Representations (TLDR), were submitted to the SemEval 2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE), where they officially ranked first on the definition modeling subtask, and achieved competitive performance on the reverse dictionary subtask. In this paper we describe our methodology and analyse our system design hypotheses.
MMG at SemEval-2022 Task 1: A Reverse Dictionary approach based on a review of the dataset from a lexicographic perspective
Authors: Alfonso Ardoiz, Miguel Ortega-Martín, Óscar García-Sierra, Jorge Álvarez, Ignacio Arranz, Adrián Alonso
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Reverse Dictionary

This paper presents a novel and linguistic-driven system for the Spanish Reverse Dictionary task of SemEval-2022 Task 1. The aim of this task is the automatic generation of a word using its gloss. The conclusion is that this task results could improve if the quality of the dataset did as well by incorporating high-quality lexicographic data. Therefore, in this paper we analyze the main gaps in the proposed dataset and describe how these limitations could be tackled.
Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions
Authors: Pinzhen Chen, Zheng Zhao
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Definition Extraction, Definition Modelling, Reverse Dictionary, Word Embeddings

This paper presents a winning submission to the SemEval 2022 Task 1 on two sub-tasks: reverse dictionary and definition modelling. We leverage a recently proposed unified model with multi-task training. It utilizes data symmetrically and learns to tackle both tracks concurrently. Analysis shows that our system performs consistently on diverse languages, and works the best with sgns embeddings. Yet, char and electra carry intriguing properties. The two tracks’ best results are always in differing subsets grouped by linguistic annotations. In this task, the quality of definition generation lags behind, and BLEU scores might be misleading.
RIGA at SemEval-2022 Task 1: Scaling Recurrent Neural Networks for CODWOE Dictionary Modeling
Authors: Eduards Mukans, Gus Strazds, Guntis Barzdins
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7

Described are our two entries “emukans” and “guntis” for the definition modeling track of CODWOE SemEval-2022 Task 1. Our approach is based on careful scaling of a GRU recurrent neural network, which exhibits double descent of errors, corresponding to significant improvements also per human judgement. Our results are in the middle of the ranking table per official automatic metrics.
Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?
Authors: Rafal Cerniavski, Sara Stymne
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Machine Translation, Reverse Dictionary, Translation, Word Embeddings

We present the Uppsala University system for SemEval-2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE). We explore the performance of multilingual reverse dictionaries as well as the possibility of utilizing annotated data in other languages to improve the quality of a reverse dictionary in the target language. We mainly focus on character-based embeddings.In our main experiment, we train multilingual models by combining the training data from multiple languages. In an additional experiment, using resources beyond the shared task, we use the training data in Russian and French to improve the English reverse dictionary using unsupervised embeddings alignment and machine translation. The results show that multilingual models occasionally but not consistently can outperform the monolingual baselines. In addition, we demonstrate an improvement of an English reverse dictionary using translated entries from the Russian training data set.
BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders
Authors: Nihed Bendahman, Julien Breton, Lina Nicolaieff, Mokhtar Boumedyen Billami, Christophe Bortolaso, Youssef Miloudi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Position, Reverse Dictionary, Word Embeddings

This paper describes our two deep learning systems that competed at SemEval-2022 Task 1 “CODWOE: Comparing Dictionaries and WOrd Embeddings”. We participated in the subtask for the reverse dictionary which consists in generating vectors from glosses. We use sequential models that integrate several neural networks, starting from Embeddings networks until the use of Dense networks, Bidirectional Long Short-Term Memory (BiLSTM) networks and LSTM networks. All glosses have been preprocessed in order to consider the best representation form of the meanings for all words that appears. We achieved very competitive results in reverse dictionary with a second position in English and French languages when using contextualized embeddings, and the same position for English, French and Spanish languages when using char embeddings.
JSI at SemEval-2022 Task 1: CODWOE - Reverse Dictionary: Monolingual and cross-lingual approaches
Authors: Thi Hong Hanh Tran, Matej Martinc, Matthew Purver, Senja Pollak
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Reverse Dictionary, Zero-Shot Learning

The reverse dictionary task is a sequence-to-vector task in which a gloss is provided as input, and the output must be a semantically matching word vector. The reverse dictionary is useful in practical applications such as solving the tip-of-the-tongue problem, helping new language learners, etc. In this paper, we evaluate the effect of a Transformer-based model with cross-lingual zero-shot learning to improve the reverse dictionary performance. Our experiments are conducted in five languages in the CODWOE dataset, including English, French, Italian, Spanish, and Russian. Even if we did not achieve a good ranking in the CODWOE competition, we show that our work partially improves the current baseline from the organizers with a hypothesis on the impact of LSTM in monolingual, multilingual, and zero-shot learning. All the codes are available at https://github.com/honghanhh/codwoe2021.
Helsinki-NLP at SemEval-2022 Task 2: A Feature-Based Approach to Multilingual Idiomaticity Detection
Authors: Sami Itkonen, Jörg Tiedemann, Mathias Creutz
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Feature Engineering, Task 2

This paper describes the University of Helsinki submission to the SemEval 2022 task on multilingual idiomaticity detection. Our system utilizes several models made available by HuggingFace, along with the baseline BERT model for the task. We focus on feature engineering based on properties that typically characterize idiomatic expressions. The additional features lead to improvements over the baseline and the final submission achieves 15th place out of 20 submissions. The paper provides error analysis of our model including visualisations of the contributions of individual features.
Hitachi at SemEval-2022 Task 2: On the Effectiveness of Span-based Classification Approaches for Multilingual Idiomaticity Detection
Authors: Atsuki Yamaguchi, Gaku Morio, Hiroaki Ozaki, Yasuhiro Sogawa
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Classification, Sentence, Sentence Embedding, Sentence-Embedding, Task 2, XLM-R

In this paper, we describe our system for SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. The task aims at detecting idiomaticity in an input sequence (Subtask A) and modeling representation of sentences that contain potential idiomatic multiword expressions (MWEs) (Subtask B) in three languages. We focus on the zero-shot setting of Subtask A and propose two span-based idiomaticity classification methods: MWE span-based classification and idiomatic MWE span prediction-based classification. We use several cross-lingual pre-trained language models (InfoXLM, XLM-R, and others) as our backbone network. Our best-performing system, fine-tuned with the span-based idiomaticity classification, ranked fifth in the zero-shot setting of Subtask A and exhibited a macro F1 score of 0.7466.
CardiffNLP-Metaphor at SemEval-2022 Task 2: Targeted Fine-tuning of Transformer-based Language Models for Idiomaticity Detection
Authors: Joanne Boisson, Jose Camacho-Collados, Luis Espinosa-Anke
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Binary Classification, Task 2

This paper describes the experiments ran for SemEval-2022 Task 2, subtask A, zero-shot and one-shot settings for idiomaticity detection. Our main approach is based on fine-tuning transformer-based language models as a baseline to perform binary classification. Our system, CardiffNLP-Metaphor, ranked 8th and 7th (respectively on zero- and one-shot settings on this task. Our main contribution lies in the extensive evaluation of transformer-based language models and various configurations, showing, among others, the potential of large multilingual models over base monolingual models. Moreover, we analyse the impact of various input parameters, which offer interesting insights on how language models work in practice.
kpfriends at SemEval-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression Recognizer
Authors: Min Sik Oh
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Sentence, Sentence Embedding, Sentence-Embedding, Task 2, Transfer Learning

We present NEAMER - Named Entity Augmented Multi-word Expression Recognizer. This system is inspired by non-compositionality characteristics shared between Named Entity and Idiomatic Expressions. We utilize transfer learning and locality features to enhance idiom classification task. This system is our submission for SemEval Task 2: Multilingual Idiomaticity Detection and Sentence Embedding Subtask A OneShot shared task. We achieve SOTA with F1 0.9395 during post-evaluation phase. We also observe improvement in training stability. Lastly, we experiment with non-compositionality knowledge transfer, cross-lingual fine-tuning and locality features, which we also introduce in this paper.
daminglu123 at SemEval-2022 Task 2: Using BERT and LSTM to Do Text Classification
Authors: Daming Lu
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Task 2, text-classification, Text Classification

Multiword expressions (MWEs) or idiomaticity are common phenomenon in natural languages. Current pre-trained language models cannot effectively capture the meaning of these MWEs. The reason is that two normal words, after combining together, could have an abruptly different meaning than the compositionality of the meanings of each word, whereas pre-trained language models reply on words compositionality. We proposed an improved method of adding an LSTM layer to the BERT model in order to get better results on a text classification task (Subtask A). Our result is slightly better than the baseline. We also tried adding TextCNN to BERT and adding both LSTM and TextCNN to BERT. We find that adding only LSTM gives the best performance.
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations
Authors: Xuange Cui, Wei Xiong, Songlin Wang
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Adversarial Attack, Contrastive Learning, Sentence, Task 2

This paper presents our contribution to the SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding.We explore the impact of three different pre-trained multilingual language models in the SubTaskA.By enhancing the model generalization and robustness, we use the exponential moving average (EMA) method and the adversarial attack strategy.In SubTaskB, we add an effective cross-attention module for modeling the relationships of two sentences.We jointly train the model with a contrastive learning objective and employ a momentum contrast to enlarge the number of negative pairs.Additionally, we use the alignment and uniformity properties to measure the quality of sentence embeddings.Our approach obtained competitive results in both subtasks.
NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection
Authors: Simone Tedeschi, Roberto Navigli
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Understanding, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embeddings, Task 2

Idioms are lexically-complex phrases whose meaning cannot be derived by compositionally interpreting their components. Although the automatic identification and understanding of idioms is essential for a wide range of Natural Language Understanding tasks, they are still largely under-investigated.This motivated the organization of the SemEval-2022 Task 2, which is divided into two multilingual subtasks: one about idiomaticity detection, and the other about sentence embeddings. In this work, we focus on the first subtask and propose a Transformer-based dual-encoder architecture to compute the semantic similarity between a potentially-idiomatic expression and its context and, based on this, predict idiomaticity. Then, we show how and to what extent Named Entity Recognition can be exploited to reduce the degree of confusion of idiom identification systems and, therefore, improve performance.Our model achieves 92.1 F1 in the one-shot setting and shows strong robustness towards unseen idioms achieving 77.4 F1 in the zero-shot setting. We release our code at https://github.com/Babelscape/ner4id.
YNU-HPCC at SemEval-2022 Task 2: Representing Multilingual Idiomaticity based on Contrastive Learning
Authors: Kuanghong Liu, Jin Wang, Xuejie Zhang
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Contrastive Learning, Sentence, Sentence Embedding, Sentence-Embedding, Task 2, Triplet

This paper will present the methods we use as the YNU-HPCC team in the SemEval-2022 Task 2, Multilingual Idiomaticity Detection and Sentence Embedding. We are involved in two subtasks, including four settings. In subtask B of sentence representation, we used novel approaches with ideas of contrastive learning to optimize model, where method of CoSENT was used in the pre-train setting, and triplet loss and multiple negatives ranking loss functions in fine-tune setting. We had achieved very competitive results on the final released test datasets. However, for subtask A of idiomaticity detection, we simply did a few explorations and experiments based on the xlm-RoBERTa model. Sentence concatenated with additional MWE as inputs did well in a one-shot setting. Sentences containing context had a poor performance on final released test data in zero-shot setting even if we attempted to extract effective information from CLS tokens of hidden layers.
SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge
Authors: Roberto Zamparelli, Shammur Chowdhury, Dominique Brunato, Cristiano Chesi, Felice Dell’Orletta, Md. Arid Hasan, Giulia Venturi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Data Augmentation

We report the results of the SemEval 2022 Task 3, PreTENS, on evaluation the acceptability of simple sentences containing constructions whose two arguments are presupposed to be or not to be in an ordered taxonomic relation. The task featured two sub-tasks articulated as: (i) binary prediction task and (ii) regression task, predicting the acceptability in a continuous scale. The sentences were artificially generated in three languages (English, Italian and French). 21 systems, with 8 system papers were submitted for the task, all based on various types of fine-tuned transformer systems, often with ensemble methods and various data augmentation techniques. The best systems reached an F1-macro score of 94.49 (sub-task1) and a Spearman correlation coefficient of 0.80 (sub-task2), with interesting variations in specific constructions and/or languages.
LingJing at SemEval-2022 Task 3: Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer
Authors: Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Bin Sun, Shutao Li, Kang Liu, Jun Zhao
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Binary Classification, Classification, regression, Transfer Learning

This paper presents the results and main findings of our system on SemEval-2022 Task 3 Presupposed Taxonomies: Evaluating Neural Network Semantics (PreTENS). This task aims at semantic competence with specific attention on the evaluation of language models, which is a task with respect to the recognition of appropriate taxonomic relations between two nominal arguments. Two sub-tasks including binary classification and regression are designed for the evaluation. For the classification sub-task, we adopt the DeBERTa-v3 pre-trained model for fine-tuning datasets of different languages. Due to the small size of the training datasets of the regression sub-task, we transfer the knowledge of classification model (i.e., model parameters) to the regression task. The experimental results show that the proposed method achieves the best results on both sub-tasks. Meanwhile, we also report negative results of multiple training strategies for further discussion. All the experimental codes are open-sourced at https://github.com/WENGSYX/Semeval.