ymoslem/acl-abstracts huggingface dataset:
Mxgra at SemEval-2020 Task 4: Common Sense Making with Next Token Prediction (2020)

In this paper, we explore solutions to a common sense making task in which a model must discern which of two sentences is against common sense. We used a pre-trained language model which we used to calculate complexity scores for input to discern which sentence contained an unlikely sequence of tokens. Other approaches we tested were word vector distances, which were used to find semantic outliers within a sentence, and siamese network. By using the pre-trained language model to calculate perplexity scores based on the sequence of tokens in input sentences, we achieved an accuracy of 75 percent.
NLP@JUST at SemEval-2020 Task 4: Ensemble Technique for BERT and Roberta to Evaluate Commonsense Validation (2020)

This paper presents the work of the NLP@JUST team at SemEval-2020 Task 4 competition that related to commonsense validation and explanation (ComVE) task. The team participates in sub-taskA (Validation) which related to validation that checks if the text is against common sense or not. Several models have trained (\textit{i.e. Bert, XLNet, and Roberta}), however, the main models used are the RoBERTa-large and BERT Whole word masking. As well as, we utilized the results from both models to generate final prediction by using the average Ensemble technique, that used to improve the overall performance. The evaluation result shows that the implemented model achieved an accuracy of 93.9{\%} obtained and published at the post-evaluation result on the leaderboard.
SSN-NLP at SemEval-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks (2020)

Common sense validation deals with testing whether a system can differentiate natural language statements that make sense from those that do not make sense. This paper describes the our approach to solve this challenge. For common sense validation with multi choice, we propose a stacking based approach to classify sentences that are more favourable in terms of common sense to the particular statement. We have used majority voting classifier methodology amongst three models such as Bidirectional Encoder Representations from Transformers (BERT), Micro Text Classification (Micro TC) and XLNet. For sentence generation, we used Neural Machine Translation (NMT) model to generate explanatory sentences.
Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning (2020)

In this paper, we present our submission for SemEval 2020 Task 4 - Commonsense Validation and Explanation (ComVE). The objective of this task was to develop a system that can differentiate statements that make sense from the ones that don`t. ComVE comprises of three subtasks to challenge and test a system`s capability in understanding commonsense knowledge from various dimensions. Commonsense reasoning is a challenging task in the domain of natural language understanding and systems augmented with it can improve performance in various other tasks such as reading comprehension, and inferencing. We have developed a system that leverages commonsense knowledge from pretrained language models trained on huge corpus such as RoBERTa, GPT2, etc. Our proposed system validates the reasonability of a given statement against the backdrop of commonsense knowledge acquired by these models and generates a logical reason to support its decision. Our system ranked 2nd in subtask C with a BLEU score of 19.3, which by far is the most challenging subtask as it required systems to generate the rationale behind the choice of an unreasonable statement. In subtask A and B, we achieved 96{\%} and 94{\%} accuracy respectively standing at 4th position in both the subtasks.
TeamJUST at SemEval-2020 Task 4: Commonsense Validation and Explanation Using Ensembling Techniques (2020)

Common sense for natural language processing methods has been attracting a wide research interest, recently. Estimating automatically whether a sentence makes sense or not is considered an essential question. Task 4 in the International Workshop SemEval 2020 has provided three subtasks (A, B, and C) that challenges the participants to build systems for distinguishing the common sense statements from those that do not make sense. This paper describes TeamJUST`s approach for participating in subtask A to differentiate between two sentences in English and classify them into two classes: common sense and uncommon sense statements. Our approach depends on ensembling four different state-of-the-art pre-trained models (BERT, ALBERT, Roberta, and XLNet). Our baseline model which we used only the pre-trained model of BERT has scored 89.1, while the TeamJUST model outperformed the baseline model with an accuracy score of 96.2. We have improved the results in the post-evaluation period to achieve our best result, which would rank the 4th in the competition if we had the chance to use our latest experiment.
TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation (2020)

In this paper, we present our submission for subtask A of the Common Sense Validation and Explanation (ComVE) shared task. We examine the ability of large-scale pre-trained language models to distinguish commonsense from non-commonsense statements. We also explore the utility of external resources that aim to supplement the world knowledge inherent in such language models, including commonsense knowledge graph embedding models, word concreteness ratings, and text-to-image generation models. We find that such resources provide insignificant gains to the performance of fine-tuned language models. We also provide a qualitative analysis of the limitations of the language model fine-tuned to this task.
UAICS at SemEval-2020 Task 4: Using a Bidirectional Transformer for Task a (2020)

Commonsense Validation and Explanation has been a difficult task for machines since the dawn of computing. Although very trivial to humans it poses a high complexity for machines due to the necessity of inference over a pre-existing knowledge base. In order to try and solve this problem the SemEval 2020 Task 4 - {\textquotedblright}Commonsense Validation and Explanation (ComVE){\textquotedblright} aims to evaluate systems capable of multiple stages of ComVE. The challenge includes 3 tasks (A, B and C), each with it`s own requirements. Our team participated only in task A which required selecting the statement that made the least sense. We choose to use a bidirectional transformer in order to solve the challenge, this paper presents the details of our method, runs and result.
UI at SemEval-2020 Task 4: Commonsense Validation and Explanation by Exploiting Contradiction (2020)

This paper describes our submissions into the ComVe challenge, the SemEval 2020 Task 4. This evaluation task consists of three sub-tasks that test commonsense comprehension by identifying sentences that do not make sense and explain why they do not. In subtask A, we use Roberta to find which sentence does not make sense. In subtask B, besides using BERT, we also experiment with replacing the dataset with MNLI when selecting the best explanation from the provided options why the given sentence does not make sense. In subtask C, we utilize the MNLI model from subtask B to evaluate the explanation generated by Roberta and GPT-2 by exploiting the contradiction of the sentence and their explanation. Our system submission records a performance of 88.2{\%}, 80.5{\%}, and BLEU 5.5 for those three subtasks, respectively.
Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation (2020)

This paper describes our system in subtask A of SemEval 2020 Shared Task 4. We propose a reinforcement learning model based on MTL(Multi-Task Learning) to enhance the prediction ability of commonsense validation. The experimental results demonstrate that our system outperforms the single-task text classification model. We combine MTL and ALBERT pretrain model to achieve an accuracy of 0.904 and our model is ranked 16th on the final leader board of the competition among the 45 teams.
YNU-oxz at SemEval-2020 Task 4: Commonsense Validation Using BERT with Bidirectional GRU (2020)

This paper describes the system and results of our team participated in SemEval-2020 Task4: Commonsense Validation and Explanation (ComVE), which aim to distinguish meaningful natural language statements from unreasonable natural language statements. This task contains three subtasks: Subtask A{--}Validation, Subtask B{--}Explanation (Multi-Choice), and Subtask C{--} Explanation (Generation). In these three subtasks, we only participated in Subtask A, which aims to distinguish whether a given two natural language statements with similar wording are meaningful. To solve this problem, we proposed a method using a combination of BERT with the Bidirectional Gated Recurrent Unit (Bi-GRU). Our model achieved an accuracy of 0.836 in Subtask A (ranked 27/45).
BLCU-NLP at SemEval-2020 Task 5: Data Augmentation for Efficient Counterfactual Detecting (2020)

Counterfactuals describe events counter to facts and hence naturally involve common sense, knowledge, and reasoning. SemEval 2020 task 5 is focusing on this field. We participate in the subtask 1 and we use BERT as our system. Our Innovations are feature extraction and data augmentation. We extract and summarize features of counterfactual statements, augment counterfactual examples in training set with the help of these features, and two general methods of data augmentation is experimented in our work. We demonstrate the effectiveness of our approaches, which achieves 0.95 of subtask 1 in F1 while using only a subset of giving training set to fine-tune the BERT model, and our official submission achieves F1 0.802, which ranks us 16th in the competition.
BYteam at SemEval-2020 Task 5: Detecting Counterfactual Statements with BERT and Ensembles (2020)

We participate in the classification tasks of SemEval-2020 Task: Subtask1: Detecting counterfactual statements of semeval-2020 task5(Detecting Counterfactuals). This paper examines different approaches and models towards detecting counterfactual statements classification. We choose the Bert model. However, the output of Bert is not a good summary of semantic information, so in order to obtain more abundant semantic information features, we modify the upper layer structure of Bert. Finally, our system achieves an accuracy of 88.90{\%} and F1 score of 86.30{\%} by hard voting, which ranks 6th on the final leader board of the in subtask 1 competition.
ETHAN at SemEval-2020 Task 5: Modelling Causal Reasoning in Language Using Neuro-symbolic Cloud Computing (2020)

I present ETHAN: Experimental Testing of Hybrid AI Node implemented entirely on free cloud computing infrastructure. The ultimate goal of this research is to create modular reusable hybrid neuro-symbolic architecture for Artificial Intelligence. As a test case I model natural language comprehension of causal relations from open domain text corpus that combines semi-supervised language model (Huggingface Transformers) with constituency and dependency parsers (Allen Institute for Artificial Intelligence.)
Ferryman as SemEval-2020 Task 5: Optimized BERT for Detecting Counterfactuals (2020)

The main purpose of this article is to state the effect of using different methods and models for counterfactual determination and detection of causal knowledge. Nowadays, counterfactual reasoning has been widely used in various fields. In the realm of natural language process(NLP), counterfactual reasoning has huge potential to improve the correctness of a sentence. In the shared Task 5 of detecting counterfactual in SemEval 2020, we pre-process the officially given dataset according to case conversion, extract stem and abbreviation replacement. We use last-5 bidirectional encoder representation from bidirectional encoder representation from transformer (BERT)and term frequency{--}inverse document frequency (TF-IDF) vectorizer for counterfactual detection. Meanwhile, multi-sample dropout and cross validation are used to improve versatility and prevent problems such as poor generosity caused by overfitting. Finally, our team Ferryman ranked the 8th place in the sub-task 1 of this competition.
ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for Counterfactual Statement Modeling (2020)

ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting counterfactual statements and detecting antecedent and consequence. This paper describes our system which is based on pretrained transformers. For the first subtask, we train several transformer-based classifiers for detecting counterfactual statements. For the second subtask, we formulate antecedent and consequence extraction as a query-based question answering problem. The two subsystems both achieved third place in the evaluation. Our system is openly released at \url{https://github.com/casnlu/ISCASSemEval2020Task5}.
Lee at SemEval-2020 Task 5: ALBERT Model Based on the Maximum Ensemble Strategy and Different Data Sampling Methods for Detecting Counterfactual Statements (2020)

This article describes the system submitted to SemEval 2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. In this task, we only participate in the subtask A which is detecting counterfactual statements. In order to solve this sub-task, first of all, because of the problem of data balance, we use the undersampling and oversampling methods to process the data set. Second, we used the ALBERT model and the maximum ensemble method based on the ALBERT model. Our methods achieved a F1 score of 0.85 in subtask A.
NLU-Co at SemEval-2020 Task 5: NLU/SVM Based Model Apply Tocharacterise and Extract Counterfactual Items on Raw Data (2020)

In this article, we try to solve the problem of classification of counterfactual statements and extraction of antecedents/consequences in raw data, by mobilizing on one hand Support vector machine (SVMs) and on the other hand Natural Language Understanding (NLU) infrastructures available on the market for conversational agents. Our experiments allowed us to test different pipelines of two known platforms (Snips NLU and Rasa NLU). The results obtained show that a Rasa NLU pipeline, built with a well-preprocessed dataset and tuned algorithms, allows to model accurately the structure of a counterfactual event, in order to facilitate the identification and the extraction of its components.
Pheonix at SemEval-2020 Task 5: Masking the Labels Lubricates Models for Sequence Labeling (2020)

This paper presents the deep-learning model that is submitted to the SemEval-2020 Task 5 competition: {\textquotedblleft}Detecting Counterfactuals{\textquotedblright}. We participated in both Subtask1 and Subtask2. The model proposed in this paper ranked 2nd in Subtask2 {\textquotedblleft}Detecting antecedent and consequence{\textquotedblright}. Our model approaches the task as a sequence labeling. The architecture is built on top of BERT, and a multi-head attention layer with label masking is used to benefit from the mutual information between nearby labels. Also, for prediction, a multi-stage algorithm is used in which the model finalize some predictions with higher certainty in each step and use them in the following. Our results show that masking the labels not only is an efficient regularization method but also improves the accuracy of the model compared with other alternatives like CRF. Label masking can be used as a regularization method in sequence labeling. Also, it improves the performance of the model by learning the specific patterns in the target variable.
YNU-oxz at SemEval-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons LSTM and Hierarchical Attention Network (2020)

This paper describes the system and results of our team`s participation in SemEval-2020 Task5: Modelling Causal Reasoning in Language: Detecting Counterfactuals, which aims to simulate counterfactual semantics and reasoning in natural language. This task contains two subtasks: Subtask1{--}Detecting counterfactual statements and Subtask2{--}Detecting antecedent and consequence. We only participated in Subtask1, aiming to determine whether a given sentence is counterfactual. In order to solve this task, we proposed a system based on Ordered Neurons LSTM (ON-LSTM) with Hierarchical Attention Network (HAN) and used Pooling operation for dimensionality reduction. Finally, we used the K-fold approach as the ensemble method. Our model achieved an F1 score of 0.7040 in Subtask1 (Ranked 16/27).
BERTatDE at SemEval-2020 Task 6: Extracting Term-definition Pairs in Free Text Using Pre-trained Model (2020)

Definition extraction is an important task in Nature Language Processing, and it is used to identify the terms and definitions related to terms. The task contains sentence classification task (i.e., classify whether it contains definition) and sequence labeling task (i.e., find the boundary of terms and definitions). The paper describes our system BERTatDE1 in sentence classification task (subtask 1) and sequence labeling task (subtask 2) in the definition extraction (SemEval-2020 Task 6). We use BERT to solve the multi-domain problems including the uncertainty of term boundary that is, different areas have different ways to definite the domain related terms. We use BERT, BiLSTM and attention in subtask 1 and our best result achieved 79.71{\%} in F1 and the eighteenth place in subtask 1. For the subtask 2, we use BERT, BiLSTM and CRF to sequence labeling, and achieve 40.73{\%} in Macro-averaged F1.
DeftPunk at SemEval-2020 Task 6: Using RNN-ensemble for the Sentence Classification. (2020)

This paper describes participation in DeftEval 2020 (part of SemEval sharing task competition), and is focused on the sentence classification. Our approach to the task was to create an ensemble of several RNNs combined with fasttext and ELMo embeddings. Results show that various types of models in an ensemble give a performance boost in comparison to standard models. Our model achieved F1-score of 78{\%} for a positive class on the DeftEval dataset.
Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction (2020)

Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.
DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction (2020)

We explore the performance of Bidirectional Encoder Representations from Transformers (BERT) at definition extraction. We further propose a joint model of BERT and Text Level Graph Convolutional Network so as to incorporate dependencies into the model. Our proposed model produces better results than BERT and achieves comparable results to BERT with fine tuned language model in DeftEval (Task 6 of SemEval 2020), a shared task of classifying whether a sentence contains a definition or not (Subtask 1).
RGCL at SemEval-2020 Task 6: Neural Approaches to DefinitionExtraction (2020)

This paper presents the RGCL team submission to SemEval 2020 Task 6: DeftEval, subtasks 1 and 2. The system classifies definitions at the sentence and token levels. It utilises state-of-the-art neural network architectures, which have some task-specific adaptations, including an automatically extended training set. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility in architecture selection.
T\"uKaPo at SemEval-2020 Task 6: Def(n)tly Not BERT: Definition Extraction Using pre-BERT Methods in a post-BERT World (2020)

We describe our system (T{\"u}KaPo) submitted for Task 6: DeftEval, at SemEval 2020. We developed a hybrid approach that combined existing CNN and RNN methods and investigated the impact of purely-syntactic and semantic features on the task of definition extraction. Our final model achieved a F1-score of 0.6851 in subtask 1, i.e, sentence classification.
UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction (2020)

Definition Extraction is the task to automatically extract terms and their definitions from text. In recent years, it attracts wide interest from NLP researchers. This paper describes the unixlong team`s system for the SemEval 2020 task6: DeftEval: Extracting term-definition pairs in free text. The goal of this task is to extract definition, word level BIO tags and relations. This task is challenging due to the free style of the text, especially the definitions of the terms range across several sentences and lack explicit verb phrases. We propose a joint model to train the tasks of definition extraction and the word level BIO tagging simultaneously. We design a creative format input of BERT to capture the location information between entity and its definition. Then we adjust the result of BERT with some rules. Finally, we apply TAG{\_}ID, ROOT{\_}ID, BIO tag to predict the relation and achieve macro-averaged F1 score 1.0 which rank first on the official test set in the relation extraction subtask.
UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction (2020)

This work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval). This competition consists of three subtasks with different levels of granularity: (1) classification of sentences as definitional or non-definitional, (2) labeling of definitional sentences, and (3) relation classification. We use various pretrained language models (i.e., BERT, XLNet, RoBERTa, SciBERT, and ALBERT) to solve each of the three subtasks of the competition. Specifically, for each language model variant, we experiment by both freezing its weights and fine-tuning them. We also explore a multi-task architecture that was trained to jointly predict the outputs for the second and the third subtasks. Our best performing model evaluated on the DeftEval dataset obtains the 32nd place for the first subtask and the 37th place for the second subtask. The code is available for further research at: \url{https://github.com/avramandrei/DeftEval}
SemEval-2020 Task 7: Assessing Humor in Edited News Headlines (2020)

This paper describes the SemEval-2020 shared task {\textquotedblleft}Assessing Humor in Edited News Headlines.{\textquotedblright} The task`s dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version. To date, this task is the most popular shared computational humor task, attracting 48 teams for the first subtask and 31 teams for the second.