ymoslem/acl-abstracts huggingface dataset:
Generating Ethnographic Models from Communities' Online Data (2020)

In this paper we describe computational ethnography study to demonstrate how machine learning techniques can be utilized to exploit bias resident in language data produced by communities with online presence. Specifically, we leverage the use of figurative language (i.e., the choice of metaphors) in online text (e.g., news media, blogs) produced by distinct communities to obtain models of community worldviews that can be shown to be distinctly biased and thus different from other communities' models. We automatically construct metaphor-based community models for two distinct scenarios: gun rights and marriage equality. We then conduct a series of experiments to validate the hypothesis that the metaphors found in each community`s online language convey the bias in the community`s worldview.
Oxymorons: a preliminary corpus investigation (2020)

This paper contains a preliminary corpus study of oxymorons, a figure of speech so far under-investigated in NLP-oriented research. The study resulted in a list of 376 oxymorons, identified by extracting a set of antonymous pairs (under various configurations) from corpora of written Italian and by manually checking the results. A complementary method is also envisaged for discovering contextual oxymorons, which are highly relevant for the detection of humor, irony and sarcasm.
Can Humor Prediction Datasets be used for Humor Generation? Humorous Headline Generation via Style Transfer (2020)

Understanding and identifying humor has been increasingly popular, as seen by the number of datasets created to study humor. However, one area of humor research, humor generation, has remained a difficult task, with machine generated jokes failing to match human-created humor. As many humor prediction datasets claim to aid in generative tasks, we examine whether these claims are true. We focus our experiments on the most popular dataset, included in the 2020 SemEval`s Task 7, and teach our model to take normal text and {\textquotedblleft}translate{\textquotedblright} it into humorous text. We evaluate our model compared to humorous human generated headlines, finding that our model is preferred equally in A/B testing with the human edited versions, a strong success for humor generation, and is preferred over an intelligent random baseline 72{\%} of the time. We also show that our model is assumed to be human written comparable with that of the human edited headlines and is significantly better than random, indicating that this dataset does indeed provide potential for future humor generation systems.
Evaluating a Bi-LSTM Model for Metaphor Detection in TOEFL Essays (2020)

This paper describes systems submitted to the Metaphor Shared Task at the Second Workshop on Figurative Language Processing. In this submission, we replicate the evaluation of the Bi-LSTM model introduced by Gao et al.(2018) on the VUA corpus in a new setting: TOEFL essays written by non-native English speakers. Our results show that Bi-LSTM models outperform feature-rich linear models on this challenging task, which is consistent with prior findings on the VUA dataset. However, the Bi-LSTM models lag behind the best performing systems in the shared task.
Neural Metaphor Detection with a Residual biLSTM-CRF Model (2020)

In this paper we present a novel resource-inexpensive architecture for metaphor detection based on a residual bidirectional long short-term memory and conditional random fields. Current approaches on this task rely on deep neural networks to identify metaphorical words, using additional linguistic features or word embeddings. We evaluate our proposed approach using different model configurations that combine embeddings, part of speech tags, and semantically disambiguated synonym sets. This evaluation process was performed using the training and testing partitions of the VU Amsterdam Metaphor Corpus. We use this method of evaluation as reference to compare the results with other current neural approaches for this task that implement similar neural architectures and features, and that were evaluated using this corpus. Results show that our system achieves competitive results with a simpler architecture compared to previous approaches.
Augmenting Neural Metaphor Detection with Concreteness (2020)

The idea that a shift in concreteness within a sentence indicates the presence of a metaphor has been around for a while. However, recent methods of detecting metaphor that have relied on deep neural models have ignored concreteness and related psycholinguistic information. We hypothesis that this information is not available to these models and that their addition will boost the performance of these models in detecting metaphor. We test this hypothesis on the Metaphor Detection Shared Task 2020 and find that the addition of concreteness information does in fact boost deep neural models. We also run tests on data from a previous shared task and show similar results.
Supervised Disambiguation of German Verbal Idioms with a BiLSTM Architecture (2020)

Supervised disambiguation of verbal idioms (VID) poses special demands on the quality and quantity of the annotated data used for learning and evaluation. In this paper, we present a new VID corpus for German and perform a series of VID disambiguation experiments on it. Our best classifier, based on a neural architecture, yields an error reduction across VIDs of 57{\%} in terms of accuracy compared to a simple majority baseline.
Metaphor Detection using Context and Concreteness (2020)

We report the results of our system on the Metaphor Detection Shared Task at the Second Workshop on Figurative Language Processing 2020. Our model is an ensemble, utilising contextualised and static distributional semantic representations, along with word-type concreteness ratings. Using these features, it predicts word metaphoricity with a deep multi-layer perceptron. We are able to best the state-of-the-art from the 2018 Shared Task by an average of 8.0{\%} F1, and finish fourth in both sub-tasks in which we participate.
Being neighbourly: Neural metaphor identification in discourse (2020)

Existing approaches to metaphor processing typically rely on local features, such as immediate lexico-syntactic contexts or information within a given sentence. However, a large body of corpus-linguistic research suggests that situational information and broader discourse properties influence metaphor production and comprehension. In this paper, we present the first neural metaphor processing architecture that models a broader discourse through the use of attention mechanisms. Our models advance the state of the art on the all POS track of the 2018 VU Amsterdam metaphor identification task. The inclusion of discourse-level information yields further significant improvements.
Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task (2020)

This paper describes the ETS entry to the 2020 Metaphor Detection shared task. Our contribution consists of a sequence of experiments using BERT, starting with a baseline, strengthening it by spell-correcting the TOEFL corpus, followed by a multi-task learning setting, where one of the tasks is the token-level metaphor classification as per the shared task, while the other is meant to provide additional training that we hypothesized to be relevant to the main task. In one case, out-of-domain data manually annotated for metaphor is used for the auxiliary task; in the other case, in-domain data automatically annotated for idioms is used for the auxiliary task. Both multi-task experiments yield promising results.
Metaphor Detection using Ensembles of Bidirectional Recurrent Neural Networks (2020)

In this paper we present our results from the Second Shared Task on Metaphor Detection, hosted by the Second Workshop on Figurative Language Processing. We use an ensemble of RNN models with bidirectional LSTMs and bidirectional attention mechanisms. Some of the models were trained on all parts of speech. Each of the other models was trained on one of four categories for parts of speech: {\textquotedblleft}nouns{\textquotedblright}, {\textquotedblleft}verbs{\textquotedblright}, {\textquotedblleft}adverbs/adjectives{\textquotedblright}, or {\textquotedblleft}other{\textquotedblright}. The models were combined into voting pools and the voting pools were combined using the logical {\textquotedblleft}OR{\textquotedblright} operator.
Metaphor Detection Using Contextual Word Embeddings From Transformers (2020)

The detection of metaphors can provide valuable information about a given text and is crucial to sentiment analysis and machine translation. In this paper, we outline the techniques for word-level metaphor detection used in our submission to the Second Shared Task on Metaphor Detection. We propose using both BERT and XLNet language models to create contextualized embeddings and a bi-directional LSTM to identify whether a given word is a metaphor. Our best model achieved F1-scores of 68.0{\%} on VUA AllPOS, 73.0{\%} on VUA Verbs, 66.9{\%} on TOEFL AllPOS, and 69.7{\%} on TOEFL Verbs, placing 7th, 6th, 5th, and 5th respectively. In addition, we outline another potential approach with a KNN-LSTM ensemble model that we did not have enough time to implement given the deadline for the competition. We show that a KNN classifier provides a similar F1-score on a validation set as the LSTM and yields different information on metaphors.
Testing the role of metadata in metaphor identification (2020)

This paper describes the adaptation and application of a neural network system for the automatic detection of metaphors. The LSTM BiRNN system participated in the shared task of metaphor identification that was part of the Second Workshop of Figurative Language Processing (FigLang2020) held at the Annual Conference of the Association for Computational Linguistics (ACL2020). The particular focus of our approach is on the potential influence that the metadata given in the ETS Corpus of Non-Native Written English might have on the automatic detection of metaphors in this dataset. The article first discusses the annotated ETS learner data, highlighting some of its peculiarities and inherent biases of metaphor use. A series of evaluations follow in order to test whether specific metadata influence the system performance in the task of automatic metaphor identification. The system is available under the APLv2 open-source license.
Sarcasm Detection Using an Ensemble Approach (2020)

We present an ensemble approach for the detection of sarcasm in Reddit and Twitter responses in the context of The Second Workshop on Figurative Language Processing held in conjunction with ACL 2020. The ensemble is trained on the predicted sarcasm probabilities of four component models and on additional features, such as the sentiment of the comment, its length, and source (Reddit or Twitter) in order to learn which of the component models is the most reliable for which input. The component models consist of an LSTM with hashtag and emoji representations; a CNN-LSTM with casing, stop word, punctuation, and sentiment representations; an MLP based on Infersent embeddings; and an SVM trained on stylometric and emotion-based features. All component models use the two conversational turns preceding the response as context, except for the SVM, which only uses features extracted from the response. The ensemble itself consists of an adaboost classifier with the decision tree algorithm as base estimator and yields F1-scores of 67{\%} and 74{\%} on the Reddit and Twitter test data, respectively.
A Transformer Approach to Contextual Sarcasm Detection in Twitter (2020)

Understanding tone in Twitter posts will be increasingly important as more and more communication moves online. One of the most difficult, yet important tones to detect is sarcasm. In the past, LSTM and transformer architecture models have been used to tackle this problem. We attempt to expand upon this research, implementing LSTM, GRU, and transformer models, and exploring new methods to classify sarcasm in Twitter posts. Among these, the most successful were transformer models, most notably BERT. While we attempted a few other models described in this paper, our most successful model was an ensemble of transformer models including BERT, RoBERTa, XLNet, RoBERTa-large, and ALBERT. This research was performed in conjunction with the sarcasm detection shared task section in the Second Workshop on Figurative Language Processing, co-located with ACL 2020.
Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media (2020)

We present a transformer-based sarcasm detection model that accounts for the context from the entire conversation thread for more robust predictions. Our model uses deep transformer layers to perform multi-head attentions among the target utterance and the relevant context in the thread. The context-aware models are evaluated on two datasets from social media, Twitter and Reddit, and show 3.1{\%} and 7.0{\%} improvements over their baselines. Our best models give the F1-scores of 79.0{\%} and 75.0{\%} for the Twitter and Reddit datasets respectively, becoming one of the highest performing systems among 36 participants in this shared task.
Simple Compounded-Label Training for Fact Extraction and Verification (2020)

Automatic fact checking is an important task motivated by the need for detecting and preventing the spread of misinformation across the web. The recently released FEVER challenge provides a benchmark task that assesses systems' capability for both the retrieval of required evidence and the identification of authentic claims. Previous approaches share a similar pipeline training paradigm that decomposes the task into three subtasks, with each component built and trained separately. Although achieving acceptable scores, these methods induce difficulty for practical application development due to unnecessary complexity and expensive computation. In this paper, we explore the potential of simplifying the system design and reducing training computation by proposing a joint training setup in which a single sequence matching model is trained with compounded labels that give supervision for both sentence selection and claim verification subtasks, eliminating the duplicate computation that occurs when models are designed and trained separately. Empirical results on FEVER indicate that our method: (1) outperforms the typical multi-task learning approach, and (2) gets comparable results to top performing systems with a much simpler training setup and less training computation (in terms of the amount of data consumed and the number of model parameters), facilitating future works on the automatic fact checking task and its practical usage.
Stance Prediction and Claim Verification: An Arabic Perspective (2020)

This work explores the application of textual entailment in news claim verification and stance prediction using a new corpus in Arabic. The publicly available corpus comes in two perspectives: a version consisting of 4,547 true and false claims and a version consisting of 3,786 pairs (claim, evidence). We describe the methodology for creating the corpus and the annotation process. Using the introduced corpus, we also develop two machine learning baselines for two proposed tasks: claim verification and stance prediction. Our best model utilizes pretraining (BERT) and achieves 76.7 F1 on the stance prediction task and 64.3 F1 on the claim verification task. Our preliminary experiments shed some light on the limits of automatic claim verification that relies on claims text only. Results hint that while the linguistic features and world knowledge learned during pretraining are useful for stance prediction, such learned representations from pretraining are insufficient for verifying claims without access to context or evidence.
A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction (2020)

Textual patterns (e.g., Country`s president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data.
Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by BERT (2020)

In the field of factoid question answering (QA), it is known that the state-of-the-art technology has achieved an accuracy comparable to that of humans in a certain benchmark challenge. On the other hand, in the area of non-factoid QA, there is still a limited number of datasets for training QA models, i.e., machine comprehension models. Considering such a situation within the field of the non-factoid QA, this paper aims to develop a dataset for training Japanese how-to tip QA models. This paper applies one of the state-of-the-art machine comprehension models to the Japanese how-to tip QA dataset. The trained how-to tip QA model is also compared with a factoid QA model trained with a Japanese factoid QA dataset. Evaluation results revealed that the how-to tip machine comprehension performance was almost comparative with that of the factoid machine comprehension even with the training data size reduced to around 4{\%} of the factoid machine comprehension. Thus, the how-to tip machine comprehension task requires much less training data compared with the factoid machine comprehension task.
Language Models as Fact Checkers? (2020)

Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data. In this paper, we leverage this implicit knowledge to create an effective end-to-end fact checker using a solely a language model, without any external knowledge or explicit retrieval components. While previous work on extracting knowledge from LMs have focused on the task of open-domain question answering, to the best of our knowledge, this is the first work to examine the use of language models as fact checkers. In a closed-book setting, we show that our zero-shot LM approach outperforms a random baseline on the standard FEVER task, and that our finetuned LM compares favorably with standard baselines. Though we do not ultimately outperform methods which use explicit knowledge bases, we believe our exploration shows that this method is viable and has much room for exploration.
Maintaining Quality in FEVER Annotation (2020)

We propose two measures for measuring the quality of constructed claims in the FEVER task. Annotating data for this task involves the creation of supporting and refuting claims over a set of evidence. Automatic annotation processes often leave superficial patterns in data, which learning systems can detect instead of performing the underlying task. Humans also can leave these superficial patterns, either voluntarily or involuntarily (due to e.g. fatigue). The two measures introduced attempt to detect the impact of these superficial patterns. One is a new information-theoretic and distributionality based measure, \textit{DCI}; and the other an extension of neural probing work over the ARCT task, \textit{utility}. We demonstrate these measures over a recent major dataset, that from the English FEVER task in 2019.
Distilling the Evidence to Augment Fact Verification Models (2020)

The alarming spread of fake news in social media, together with the impossibility of scaling manual fact verification, motivated the development of natural language processing techniques to automatically verify the veracity of claims. Most approaches perform a claim-evidence classification without providing any insights about why the claim is trustworthy or not. We propose, instead, a model-agnostic framework that consists of two modules: (1) a span extractor, which identifies the crucial information connecting claim and evidence; and (2) a classifier that combines claim, evidence, and the extracted spans to predict the veracity of the claim. We show that the spans are informative for the classifier, improving performance and robustness. Tested on several state-of-the-art models over the Fever dataset, the enhanced classifiers consistently achieve higher accuracy while also showing reduced sensitivity to artifacts in the claims.
A proof of concept on triangular test evaluation for Natural Language Generation (2020)

The evaluation of Natural Language Generation (NLG) systems has recently aroused much interest in the research community, since it should address several challenging aspects, such as readability of the generated texts, adequacy to the user within a particular context and moment and linguistic quality-related issues (e.g., correctness, coherence, understandability), among others. In this paper, we propose a novel technique for evaluating NLG systems that is inspired on the triangular test used in the field of sensory analysis. This technique allows us to compare two texts generated by different subjects and to i) determine whether statistically significant differences are detected between them when evaluated by humans and ii) quantify to what extent the number of evaluators plays an important role in the sensitivity of the results. As a proof of concept, we apply this evaluation technique in a real use case in the field of meteorology, showing the advantages and disadvantages of our proposal.
\textquotedblleftThis is a Problem, Don`t You Agree?\textquotedblright Framing and Bias in Human Evaluation for Natural Language Generation (2020)

Despite recent efforts reviewing current human evaluation practices for natural language generation (NLG) research, the lack of reported question wording and potential for framing effects or cognitive biases influencing results has been widely overlooked. In this opinion paper, we detail three possible framing effects and cognitive biases that could be imposed on human evaluation in NLG. Based on this, we make a call for increased transparency for human evaluation in NLG and propose the concept of human evaluation statements. We make several recommendations for design details to report that could potentially influence results, such as question wording, and suggest that reporting pertinent design details can help increase comparability across studies as well as reproducibility of results.
Evaluation rules! On the use of grammars and rule-based systems for NLG evaluation (2020)

NLG researchers often use uncontrolled corpora to train and evaluate their systems, using textual similarity metrics, such as BLEU. This position paper argues in favour of two alternative evaluation strategies, using grammars or rule-based systems. These strategies are particularly useful to identify the strengths and weaknesses of different systems. We contrast our proposals with the (extended) WebNLG dataset, which is revealed to have a skewed distribution of predicates. We predict that this distribution affects the quality of the predictions for systems trained on this data. However, this hypothesis can only be thoroughly tested (without any confounds) once we are able to systematically manipulate the skewness of the data, using a rule-based approach.