Noam Slonim


2023

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Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy
Shai Gretz | Assaf Toledo | Roni Friedman | Dan Lahav | Rose Weeks | Naor Bar-Zeev | João Sedoc | Pooja Sangha | Yoav Katz | Noam Slonim
Findings of the Association for Computational Linguistics: EACL 2023

The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-19 vaccines. Here, we release VIRADialogs, a dataset of over 8k dialogues conducted by actual users with VIRA, providing a unique real-world conversational dataset. In light of rapid changes in users’ intents, due to updates in guidelines or in response to new information, we highlight the important task of intent discovery in this use-case. We introduce a novel automatic evaluation framework for intent discovery, leveraging the existing intent classifier of VIRA. We use this framework to report baseline intent discovery results over VIRADialogs, that highlight the difficulty of this task.

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Knowledge is a Region in Weight Space for Fine-tuned Language Models
Almog Gueta | Elad Venezian | Colin Raffel | Noam Slonim | Yoav Katz | Leshem Choshen
Findings of the Association for Computational Linguistics: EMNLP 2023

Research on neural networks has focused on understanding a single model trained on a single dataset. However, relatively little is known about the relationships between different models, particularly those trained or tested on different datasets. We address this by studying how the weight space and the underlying loss landscape of different models are interconnected. Specifically, we demonstrate that finetuned models that were optimized for high performance, reside in well-defined regions in weight space, and vice versa – that any model that resides anywhere in those regions also exhibits high performance. Notably, we show that language models that have been finetuned on the same dataset form a tight cluster in the weight space, while models finetuned on different datasets from the same underlying task form a looser cluster. Moreover, traversing around the region between the models leads to new models that perform comparably or even better than models obtained via finetuning, even on tasks that the original models were not finetuned on. Our findings provide insight into the relationships between models, demonstrating that a model positioned between two similar models can acquire the knowledge of both. We leverage this and design a method for selecting a better model for efficient finetuning. Specifically, we show that starting from the center of the region is as effective, if not more, than using the pretrained model in 11 out of 12 datasets, resulting in an average accuracy improvement of 3.06.

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Zero-shot Topical Text Classification with LLMs - an Experimental Study
Shai Gretz | Alon Halfon | Ilya Shnayderman | Orith Toledo-Ronen | Artem Spector | Lena Dankin | Yannis Katsis | Ofir Arviv | Yoav Katz | Noam Slonim | Liat Ein-Dor
Findings of the Association for Computational Linguistics: EMNLP 2023

Topical Text Classification (TTC) is an ancient, yet timely research area in natural language processing, with many practical applications. The recent dramatic advancements in large LMs raise the question of how well these models can perform in this task in a zero-shot scenario. Here, we share a first comprehensive study, comparing the zero-shot performance of a variety of LMs over TTC23, a large benchmark collection of 23 publicly available TTC datasets, covering a wide range of domains and styles. In addition, we leverage this new TTC benchmark to create LMs that are specialized in TTC, by fine-tuning these LMs over a subset of the datasets and evaluating their performance over the remaining, held-out datasets. We show that the TTC-specialized LMs obtain the top performance on our benchmark, by a significant margin. Our code and model are made available for the community. We hope that the results presented in this work will serve as a useful guide for practitioners interested in topical text classification.

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Where to start? Analyzing the potential value of intermediate models
Leshem Choshen | Elad Venezian | Shachar Don-Yehiya | Noam Slonim | Yoav Katz
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Previous studies observed that finetuned models may be better base models than the vanilla pretrained model. Such a model, finetuned on some source dataset, may provide a better starting point for a new finetuning process on a desired target dataset. Here, we perform a systematic analysis of this intertraining scheme, over a wide range of English classification tasks. Surprisingly, our analysis suggests that the potential intertraining gain can be analyzed independently for the target dataset under consideration, and for a base model being considered as a starting point. Hence, a performant model is generally strong, even if its training data was not aligned with the target dataset. Furthermore, we leverage our analysis to propose a practical and efficient approach to determine if and how to select a base model in real-world settings. Last, we release an updating ranking of best models in the HuggingFace hub per architecture.

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Active Learning for Natural Language Generation
Yotam Perlitz | Ariel Gera | Michal Shmueli-Scheuer | Dafna Sheinwald | Noam Slonim | Liat Ein-Dor
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

The field of Natural Language Generation (NLG) suffers from a severe shortage of labeled data due to the extremely expensive and time-consuming process involved in manual annotation. A natural approach for coping with this problem is active learning (AL), a well-known machine learning technique for improving annotation efficiency by selectively choosing the most informative examples to label. However, while AL has been well-researched in the context of text classification, its application to NLG remains largely unexplored. In this paper, we present a first systematic study of active learning for NLG, considering a diverse set of tasks and multiple leading selection strategies, and harnessing a strong instruction-tuned model. Our results indicate that the performance of existing AL strategies is inconsistent, surpassing the baseline of random example selection in some cases but not in others. We highlight some notable differences between the classification and generation scenarios, and analyze the selection behaviors of existing AL strategies. Our findings motivate exploring novel approaches for applying AL to generation tasks.

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Welcome to the Real World: Efficient, Incremental and Scalable Key Point Analysis
Lilach Eden | Yoav Kantor | Matan Orbach | Yoav Katz | Noam Slonim | Roy Bar-Haim
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

Key Point Analysis (KPA) is an emerging summarization framework, which extracts the main points from a collection of opinions, and quantifies their prevalence. It has been successfully applied to diverse types of data, including arguments, user reviews and survey responses. Despite the growing academic interest in KPA, little attention has been given to the practical challenges of implementing a KPA system in production. This work presents a deployed KPA system, which regularly serves multiple teams in our organization. We discuss the main challenges we faced while building a real-world KPA system, as well as the architecture and algorithmic improvements we developed to address these challenges. Specifically, we focus on efficient matching of sentences to key points, incremental processing, scalability and resiliency. The value of our contributions is demonstrated in an extensive set of experiments, over five existing and novel datasets. Finally, we describe several use cases of the deployed system, which illustrate its practical value.

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Efficient Methods for Natural Language Processing: A Survey
Marcos Treviso | Ji-Ung Lee | Tianchu Ji | Betty van Aken | Qingqing Cao | Manuel R. Ciosici | Michael Hassid | Kenneth Heafield | Sara Hooker | Colin Raffel | Pedro H. Martins | André F. T. Martins | Jessica Zosa Forde | Peter Milder | Edwin Simpson | Noam Slonim | Jesse Dodge | Emma Strubell | Niranjan Balasubramanian | Leon Derczynski | Iryna Gurevych | Roy Schwartz
Transactions of the Association for Computational Linguistics, Volume 11

Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.

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ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning
Shachar Don-Yehiya | Elad Venezian | Colin Raffel | Noam Slonim | Leshem Choshen
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask training by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.19 points on average without any changes to the architecture.

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The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers
Ariel Gera | Roni Friedman | Ofir Arviv | Chulaka Gunasekara | Benjamin Sznajder | Noam Slonim | Eyal Shnarch
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative. In this work, we argue that due to the gradual improvement across model layers, additional information can be gleaned from the contrast between higher and lower layers during inference. Specifically, in choosing between the probable next token predictions of a generative model, the predictions of lower layers can be used to highlight which candidates are best avoided. We propose a novel approach that utilizes the contrast between layers to improve text generation outputs, and show that it mitigates degenerative behaviors of the model in open-ended generation, significantly improving the quality of generated texts. Furthermore, our results indicate that contrasting between model layers at inference time can yield substantial benefits to certain aspects of general language model capabilities, more effectively extracting knowledge during inference from a given set of model parameters.

2022

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Quality Controlled Paraphrase Generation
Elron Bandel | Ranit Aharonov | Michal Shmueli-Scheuer | Ilya Shnayderman | Noam Slonim | Liat Ein-Dor
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, the original sentence. Generating high-quality paraphrases is challenging as it becomes increasingly hard to preserve meaning as linguistic diversity increases. Recent works achieve nice results by controlling specific aspects of the paraphrase, such as its syntactic tree. However, they do not allow to directly control the quality of the generated paraphrase, and suffer from low flexibility and scalability. Here we propose QCPG, a quality-guided controlled paraphrase generation model, that allows directly controlling the quality dimensions. Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases. We show that our method is able to generate paraphrases which maintain the original meaning while achieving higher diversity than the uncontrolled baseline. The models, the code, and the data can be found in https://github.com/IBM/quality-controlled-paraphrase-generation.

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Cluster & Tune: Boost Cold Start Performance in Text Classification
Eyal Shnarch | Ariel Gera | Alon Halfon | Lena Dankin | Leshem Choshen | Ranit Aharonov | Noam Slonim
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. In such cases, the common practice of fine-tuning pre-trained models, such as BERT, for a target classification task, is prone to produce poor performance. We suggest a method to boost the performance of such models by adding an intermediate unsupervised classification task, between the pre-training and fine-tuning phases. As such an intermediate task, we perform clustering and train the pre-trained model on predicting the cluster labels. We test this hypothesis on various data sets, and show that this additional classification phase can significantly improve performance, mainly for topical classification tasks, when the number of labeled instances available for fine-tuning is only a couple of dozen to a few hundred.

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Multi-Domain Targeted Sentiment Analysis
Orith Toledo-Ronen | Matan Orbach | Yoav Katz | Noam Slonim
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Targeted Sentiment Analysis (TSA) is a central task for generating insights from consumer reviews. Such content is extremely diverse, with sites like Amazon or Yelp containing reviews on products and businesses from many different domains. A real-world TSA system should gracefully handle that diversity. This can be achieved by a multi-domain model – one that is robust to the domain of the analyzed texts, and performs well on various domains. To address this scenario, we present a multi-domain TSA system based on augmenting a given training set with diverse weak labels from assorted domains. These are obtained through self-training on the Yelp reviews corpus. Extensive experiments with our approach on three evaluation datasets across different domains demonstrate the effectiveness of our solution. We further analyze how restrictions imposed on the available labeled data affect the performance, and compare the proposed method to the costly alternative of manually gathering diverse TSA labeled data. Our results and analysis show that our approach is a promising step towards a practical domain-robust TSA system.

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Zero-Shot Text Classification with Self-Training
Ariel Gera | Alon Halfon | Eyal Shnarch | Yotam Perlitz | Liat Ein-Dor | Noam Slonim
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Recent advances in large pretrained language models have increased attention to zero-shot text classification. In particular, models finetuned on natural language inference datasets have been widely adopted as zero-shot classifiers due to their promising results and off-the-shelf availability. However, the fact that such models are unfamiliar with the target task can lead to instability and performance issues. We propose a plug-and-play method to bridge this gap using a simple self-training approach, requiring only the class names along with an unlabeled dataset, and without the need for domain expertise or trial and error. We show that fine-tuning the zero-shot classifier on its most confident predictions leads to significant performance gains across a wide range of text classification tasks, presumably since self-training adapts the zero-shot model to the task at hand.

2021

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Overview of the 2021 Key Point Analysis Shared Task
Roni Friedman | Lena Dankin | Yufang Hou | Ranit Aharonov | Yoav Katz | Noam Slonim
Proceedings of the 8th Workshop on Argument Mining

We describe the 2021 Key Point Analysis (KPA-2021) shared task on key point analysis that we organized as a part of the 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP 2021. We outline various approaches and discuss the results of the shared task. We expect the task and the findings reported in this paper to be relevant for researchers working on text summarization and argument mining.

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YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews
Matan Orbach | Orith Toledo-Ronen | Artem Spector | Ranit Aharonov | Yoav Katz | Noam Slonim
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Current TSA evaluation in a cross-domain setup is restricted to the small set of review domains available in existing datasets. Such an evaluation is limited, and may not reflect true performance on sites like Amazon or Yelp that host diverse reviews from many domains. To address this gap, we present YASO – a new TSA evaluation dataset of open-domain user reviews. YASO contains 2,215 English sentences from dozens of review domains, annotated with target terms and their sentiment. Our analysis verifies the reliability of these annotations, and explores the characteristics of the collected data. Benchmark results using five contemporary TSA systems show there is ample room for improvement on this challenging new dataset. YASO is available at https://github.com/IBM/yaso-tsa.

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Project Debater APIs: Decomposing the AI Grand Challenge
Roy Bar-Haim | Yoav Kantor | Elad Venezian | Yoav Katz | Noam Slonim
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Project Debater was revealed in 2019 as the first AI system that can debate human experts on complex topics. Engaging in a live debate requires a diverse set of skills, and Project Debater has been developed accordingly as a collection of components, each designed to perform a specific subtask. Project Debater APIs provide access to many of these capabilities, as well as to more recently developed ones. This diverse set of web services, publicly available for academic use, includes core NLP services, argument mining and analysis capabilities, and higher-level services for content summarization. We describe these APIs and their performance, and demonstrate how they can be used for building practical solutions. In particular, we will focus on Key Point Analysis, a novel technology that identifies the main points and their prevalence in a collection of texts such as survey responses and user reviews.

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Every Bite Is an Experience: Key Point Analysis of Business Reviews
Roy Bar-Haim | Lilach Eden | Yoav Kantor | Roni Friedman | Noam Slonim
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views. Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data. We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. We show empirically that these novel extensions of KPA substantially improve its performance. We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.

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Advances in Debating Technologies: Building AI That Can Debate Humans
Roy Bar-Haim | Liat Ein-Dor | Matan Orbach | Elad Venezian | Noam Slonim
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts

The tutorial focuses on Debating Technologies, a sub-field of computational argumentation defined as “computational technologies developed directly to enhance, support, and engage with human debating” (Gurevych et al., 2016). A recent milestone in this field is Project Debater, which was revealed in 2019 as the first AI system that can debate human experts on complex topics. Project Debater is the third in the series of IBM Research AI’s grand challenges, following Deep Blue and Watson. It has been developed for over six years by a large team of researchers and engineers, and its live demonstration in February 2019 received massive media attention. This research effort has resulted in more than 50 scientific papers to date, and many datasets freely available for research purposes. We discuss the scientific challenges that arise when building such a system, including argument mining, argument quality assessment, stance classification, principled argument detection, narrative generation, and rebutting a human opponent. Many of the underlying capabilities of Project Debater have been made freely available for academic research, and the tutorial will include a detailed explanation of how to use and leverage these tools. In addition to discussing individual components, the tutorial also provides a holistic view of a debating system. Such a view is largely missing in the academic literature, where each paper typically addresses a specific problem in isolation. We present a complete pipeline of a debating system, and discuss the information flow and the interaction between the various components. Finally, we discuss practical applications and future challenges of debating technologies.

2020

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Quantitative argument summarization and beyond: Cross-domain key point analysis
Roy Bar-Haim | Yoav Kantor | Lilach Eden | Roni Friedman | Dan Lahav | Noam Slonim
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence. Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect. Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments. The current work advances key point analysis in two important respects: first, we develop a method for automatic extraction of key points, which enables fully automatic analysis, and is shown to achieve performance comparable to a human expert. Second, we demonstrate that the applicability of key point analysis goes well beyond argumentation data. Using models trained on publicly available argumentation datasets, we achieve promising results in two additional domains: municipal surveys and user reviews. An additional contribution is an in-depth evaluation of argument-to-key point matching models, where we substantially outperform previous results.

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Active Learning for BERT: An Empirical Study
Liat Ein-Dor | Alon Halfon | Ariel Gera | Eyal Shnarch | Lena Dankin | Leshem Choshen | Marina Danilevsky | Ranit Aharonov | Yoav Katz | Noam Slonim
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Real world scenarios present a challenge for text classification, since labels are usually expensive and the data is often characterized by class imbalance. Active Learning (AL) is a ubiquitous paradigm to cope with data scarcity. Recently, pre-trained NLP models, and BERT in particular, are receiving massive attention due to their outstanding performance in various NLP tasks. However, the use of AL with deep pre-trained models has so far received little consideration. Here, we present a large-scale empirical study on active learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets. We focus on practical scenarios of binary text classification, where the annotation budget is very small, and the data is often skewed. Our results demonstrate that AL can boost BERT performance, especially in the most realistic scenario in which the initial set of labeled examples is created using keyword-based queries, resulting in a biased sample of the minority class. We release our research framework, aiming to facilitate future research along the lines explored here.

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Multilingual Argument Mining: Datasets and Analysis
Orith Toledo-Ronen | Matan Orbach | Yonatan Bilu | Artem Spector | Noam Slonim
Findings of the Association for Computational Linguistics: EMNLP 2020

The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However, as with many other NLU tasks, the dominant language is English, with resources in other languages being few and far between. In this work, we explore the potential of transfer learning using the multilingual BERT model to address argument mining tasks in non-English languages, based on English datasets and the use of machine translation. We show that such methods are well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments, presumably because quality is harder to preserve under translation. In addition, focusing on the translate-train approach, we show how the choice of languages for translation, and the relations among them, affect the accuracy of the resultant model. Finally, to facilitate evaluation of transfer learning on argument mining tasks, we provide a human-generated dataset with more than 10k arguments in multiple languages, as well as machine translation of the English datasets.

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The workweek is the best time to start a family – A Study of GPT-2 Based Claim Generation
Shai Gretz | Yonatan Bilu | Edo Cohen-Karlik | Noam Slonim
Findings of the Association for Computational Linguistics: EMNLP 2020

Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information. Here we suggest a pipeline based on GPT-2 for generating coherent claims, and explore the types of claims that it produces, and their veracity, using an array of manual and automatic assessments. In addition, we explore the interplay between this task and the task of Claim Retrieval, showing how they can complement one another.

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Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains
Eyal Shnarch | Leshem Choshen | Guy Moshkowich | Ranit Aharonov | Noam Slonim
Findings of the Association for Computational Linguistics: EMNLP 2020

Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory. Aiming to assist domain experts in their first steps into a new task over a new corpus, we present an unsupervised approach to reveal complex rules which cluster the unexplored corpus by its prominent categories (or facets). These rules are human-readable, thus providing an important ingredient which has become in short supply lately - explainability. Each rule provides an explanation for the commonality of all the texts it clusters together. The experts can then identify which rules best capture texts of their categories of interest, and utilize them to deepen their understanding of these categories. These rules can also bootstrap the process of data labeling by pointing at a subset of the corpus which is enriched with texts demonstrating the target categories. We present an extensive evaluation of the usefulness of these rules in identifying target categories, as well as a user study which assesses their interpretability.

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From Arguments to Key Points: Towards Automatic Argument Summarization
Roy Bar-Haim | Lilach Eden | Roni Friedman | Yoav Kantor | Dan Lahav | Noam Slonim
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem. We propose to represent such summaries as a small set of talking points, termed key points, each scored according to its salience. We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments. Furthermore, we found that a domain expert can often predict these key points in advance. We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task. We report empirical results for an extensive set of experiments with this dataset, showing promising performance.

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Out of the Echo Chamber: Detecting Countering Debate Speeches
Matan Orbach | Yonatan Bilu | Assaf Toledo | Dan Lahav | Michal Jacovi | Ranit Aharonov | Noam Slonim
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in “echo chambers” and may fall prey to fake news and disinformation, lacking easy access to dissenting views. We suggest a novel task aiming to alleviate some of these concerns – that of detecting articles that most effectively counter the arguments – and not just the stance – made in a given text. We study this problem in the context of debate speeches. Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it. We provide a large dataset of 3,685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community. We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research. All data collected during this work is freely available for research.

2019

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Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network
Martin Gleize | Eyal Shnarch | Leshem Choshen | Lena Dankin | Guy Moshkowich | Ranit Aharonov | Noam Slonim
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing alternatives. We also propose a Siamese neural network architecture shown to outperform several baselines on both a prior convincingness data set and our own. Finally, we provide insights into our experimental results and the various kinds of argumentative value our method is capable of detecting.

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From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion
Roy Bar-Haim | Dalia Krieger | Orith Toledo-Ronen | Lilach Edelstein | Yonatan Bilu | Alon Halfon | Yoav Katz | Amir Menczel | Ranit Aharonov | Noam Slonim
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

When debating a controversial topic, it is often desirable to expand the boundaries of discussion. For example, we may consider the pros and cons of possible alternatives to the debate topic, make generalizations, or give specific examples. We introduce the task of Debate Topic Expansion - finding such related topics for a given debate topic, along with a novel annotated dataset for the task. We focus on relations between Wikipedia concepts, and show that they differ from well-studied lexical-semantic relations such as hypernyms, hyponyms and antonyms. We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically. We suggest that debate topic expansion may have various use cases in argumentation mining.

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Argument Invention from First Principles
Yonatan Bilu | Ariel Gera | Daniel Hershcovich | Benjamin Sznajder | Dan Lahav | Guy Moshkowich | Anael Malet | Assaf Gavron | Noam Slonim
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Competitive debaters often find themselves facing a challenging task – how to debate a topic they know very little about, with only minutes to prepare, and without access to books or the Internet? What they often do is rely on ”first principles”, commonplace arguments which are relevant to many topics, and which they have refined in past debates. In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic. As far as we know, this is the first time that this approach to argument invention is formalized and made explicit in the context of NLP. The main goal of this work is to show that it is possible to define such a taxonomy. While the taxonomy suggested here should be thought of as a ”first attempt” it is nonetheless coherent, covers well the relevant topics and coincides with what professional debaters actually argue in their speeches, and facilitates automatic argument invention for new topics.

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A Dataset of General-Purpose Rebuttal
Matan Orbach | Yonatan Bilu | Ariel Gera | Yoav Kantor | Lena Dankin | Tamar Lavee | Lili Kotlerman | Shachar Mirkin | Michal Jacovi | Ranit Aharonov | Noam Slonim
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel task of producing a critical response to a long argumentative text, and suggest a method based on general rebuttal arguments to address it. We do this in the context of the recently-suggested task of listening comprehension over argumentative content: given a speech on some specified topic, and a list of relevant arguments, the goal is to determine which of the arguments appear in the speech. The general rebuttals we describe here (in English) overcome the need for topic-specific arguments to be provided, by proving to be applicable for a large set of topics. This allows creating responses beyond the scope of topics for which specific arguments are available. All data collected during this work is freely available for research.

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Automatic Argument Quality Assessment - New Datasets and Methods
Assaf Toledo | Shai Gretz | Edo Cohen-Karlik | Roni Friedman | Elad Venezian | Dan Lahav | Michal Jacovi | Ranit Aharonov | Noam Slonim
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.

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Financial Event Extraction Using Wikipedia-Based Weak Supervision
Liat Ein-Dor | Ariel Gera | Orith Toledo-Ronen | Alon Halfon | Benjamin Sznajder | Lena Dankin | Yonatan Bilu | Yoav Katz | Noam Slonim
Proceedings of the Second Workshop on Economics and Natural Language Processing

Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledge-base of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.

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Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation
Tamar Lavee | Lili Kotlerman | Matan Orbach | Yonatan Bilu | Michal Jacovi | Ranit Aharonov | Noam Slonim
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP

Recent advancements in machine reading and listening comprehension involve the annotation of long texts. Such tasks are typically time consuming, making crowd-annotations an attractive solution, yet their complexity often makes such a solution unfeasible. In particular, a major concern is that crowd annotators may be tempted to skim through long texts, and answer questions without reading thoroughly. We present a case study of adapting this type of task to the crowd. The task is to identify claims in a several minute long debate speech. We show that sentence-by-sentence annotation does not scale and that labeling only a subset of sentences is insufficient. Instead, we propose a scheme for effectively performing the full, complex task with crowd annotators, allowing the collection of large scale annotated datasets. We believe that the encountered challenges and pitfalls, as well as lessons learned, are relevant in general when collecting data for large scale natural language understanding (NLU) tasks.

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Syntactic Interchangeability in Word Embedding Models
Daniel Hershcovich | Assaf Toledo | Alon Halfon | Noam Slonim
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP

Nearest neighbors in word embedding models are commonly observed to be semantically similar, but the relations between them can vary greatly. We investigate the extent to which word embedding models preserve syntactic interchangeability, as reflected by distances between word vectors, and the effect of hyper-parameters—context window size in particular. We use part of speech (POS) as a proxy for syntactic interchangeability, as generally speaking, words with the same POS are syntactically valid in the same contexts. We also investigate the relationship between interchangeability and similarity as judged by commonly-used word similarity benchmarks, and correlate the result with the performance of word embedding models on these benchmarks. Our results will inform future research and applications in the selection of word embedding model, suggesting a principle for an appropriate selection of the context window size parameter depending on the use-case.

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Learning to combine Grammatical Error Corrections
Yoav Kantor | Yoav Katz | Leshem Choshen | Edo Cohen-Karlik | Naftali Liberman | Assaf Toledo | Amir Menczel | Noam Slonim
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

The field of Grammatical Error Correction (GEC) has produced various systems to deal with focused phenomena or general text editing. We propose an automatic way to combine black-box systems. Our method automatically detects the strength of a system or the combination of several systems per error type, improving precision and recall while optimizing F-score directly. We show consistent improvement over the best standalone system in all the configurations tested. This approach also outperforms average ensembling of different RNN models with random initializations. In addition, we analyze the use of BERT for GEC - reporting promising results on this end. We also present a spellchecker created for this task which outperforms standard spellcheckers tested on the task of spellchecking. This paper describes a system submission to Building Educational Applications 2019 Shared Task: Grammatical Error Correction. Combining the output of top BEA 2019 shared task systems using our approach, currently holds the highest reported score in the open phase of the BEA 2019 shared task, improving F-0.5 score by 3.7 points over the best result reported.

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Towards Effective Rebuttal: Listening Comprehension Using Corpus-Wide Claim Mining
Tamar Lavee | Matan Orbach | Lili Kotlerman | Yoav Kantor | Shai Gretz | Lena Dankin | Michal Jacovi | Yonatan Bilu | Ranit Aharonov | Noam Slonim
Proceedings of the 6th Workshop on Argument Mining

Engaging in a live debate requires, among other things, the ability to effectively rebut arguments claimed by your opponent. In particular, this requires identifying these arguments. Here, we suggest doing so by automatically mining claims from a corpus of news articles containing billions of sentences, and searching for them in a given speech. This raises the question of whether such claims indeed correspond to those made in spoken speeches. To this end, we collected a large dataset of 400 speeches in English discussing 200 controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech. Results show that in the vast majority of speeches debaters indeed make use of such claims. In addition, we present several baselines for the automatic detection of mined claims in speeches, forming the basis for future work. All collected data is freely available for research.

2018

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Proceedings of the 5th Workshop on Argument Mining
Noam Slonim | Ranit Aharonov
Proceedings of the 5th Workshop on Argument Mining

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Towards an argumentative content search engine using weak supervision
Ran Levy | Ben Bogin | Shai Gretz | Ranit Aharonov | Noam Slonim
Proceedings of the 27th International Conference on Computational Linguistics

Searching for sentences containing claims in a large text corpus is a key component in developing an argumentative content search engine. Previous works focused on detecting claims in a small set of documents or within documents enriched with argumentative content. However, pinpointing relevant claims in massive unstructured corpora, received little attention. A step in this direction was taken in (Levy et al. 2017), where the authors suggested using a weak signal to develop a relatively strict query for claim–sentence detection. Here, we leverage this work to define weak signals for training DNNs to obtain significantly greater performance. This approach allows to relax the query and increase the potential coverage. Our results clearly indicate that the system is able to successfully generalize from the weak signal, outperforming previously reported results in terms of both precision and coverage. Finally, we adapt our system to solve a recent argument mining task of identifying argumentative sentences in Web texts retrieved from heterogeneous sources, and obtain F1 scores comparable to the supervised baseline.

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Learning Sentiment Composition from Sentiment Lexicons
Orith Toledo-Ronen | Roy Bar-Haim | Alon Halfon | Charles Jochim | Amir Menczel | Ranit Aharonov | Noam Slonim
Proceedings of the 27th International Conference on Computational Linguistics

Sentiment composition is a fundamental sentiment analysis problem. Previous work relied on manual rules and manually-created lexical resources such as negator lists, or learned a composition function from sentiment-annotated phrases or sentences. We propose a new approach for learning sentiment composition from a large, unlabeled corpus, which only requires a word-level sentiment lexicon for supervision. We automatically generate large sentiment lexicons of bigrams and unigrams, from which we induce a set of lexicons for a variety of sentiment composition processes. The effectiveness of our approach is confirmed through manual annotation, as well as sentiment classification experiments with both phrase-level and sentence-level benchmarks.

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A Recorded Debating Dataset
Shachar Mirkin | Michal Jacovi | Tamar Lavee | Hong-Kwang Kuo | Samuel Thomas | Leslie Sager | Lili Kotlerman | Elad Venezian | Noam Slonim
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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SLIDE - a Sentiment Lexicon of Common Idioms
Charles Jochim | Francesca Bonin | Roy Bar-Haim | Noam Slonim
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Semantic Relatedness of Wikipedia Concepts – Benchmark Data and a Working Solution
Liat Ein Dor | Alon Halfon | Yoav Kantor | Ran Levy | Yosi Mass | Ruty Rinott | Eyal Shnarch | Noam Slonim
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Listening Comprehension over Argumentative Content
Shachar Mirkin | Guy Moshkowich | Matan Orbach | Lili Kotlerman | Yoav Kantor | Tamar Lavee | Michal Jacovi | Yonatan Bilu | Ranit Aharonov | Noam Slonim
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.

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Learning Concept Abstractness Using Weak Supervision
Ella Rabinovich | Benjamin Sznajder | Artem Spector | Ilya Shnayderman | Ranit Aharonov | David Konopnicki | Noam Slonim
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data. Exploiting only minimal linguistic clues and the contextual usage of a concept as manifested in textual data, we train sufficiently powerful classifiers, obtaining high correlation with human labels. The results imply the applicability of this approach to additional properties of concepts, additional languages, and resource-scarce scenarios.

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Learning Thematic Similarity Metric from Article Sections Using Triplet Networks
Liat Ein Dor | Yosi Mass | Alon Halfon | Elad Venezian | Ilya Shnayderman | Ranit Aharonov | Noam Slonim
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

In this paper we suggest to leverage the partition of articles into sections, in order to learn thematic similarity metric between sentences. We assume that a sentence is thematically closer to sentences within its section than to sentences from other sections. Based on this assumption, we use Wikipedia articles to automatically create a large dataset of weakly labeled sentence triplets, composed of a pivot sentence, one sentence from the same section and one from another section. We train a triplet network to embed sentences from the same section closer. To test the performance of the learned embeddings, we create and release a sentence clustering benchmark. We show that the triplet network learns useful thematic metrics, that significantly outperform state-of-the-art semantic similarity methods and multipurpose embeddings on the task of thematic clustering of sentences. We also show that the learned embeddings perform well on the task of sentence semantic similarity prediction.

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Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining
Eyal Shnarch | Carlos Alzate | Lena Dankin | Martin Gleize | Yufang Hou | Leshem Choshen | Ranit Aharonov | Noam Slonim
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

The process of obtaining high quality labeled data for natural language understanding tasks is often slow, error-prone, complicated and expensive. With the vast usage of neural networks, this issue becomes more notorious since these networks require a large amount of labeled data to produce satisfactory results. We propose a methodology to blend high quality but scarce strong labeled data with noisy but abundant weak labeled data during the training of neural networks. Experiments in the context of topic-dependent evidence detection with two forms of weak labeled data show the advantages of the blending scheme. In addition, we provide a manually annotated data set for the task of topic-dependent evidence detection. We believe that blending weak and strong labeled data is a general notion that may be applicable to many language understanding tasks, and can especially assist researchers who wish to train a network but have a small amount of high quality labeled data for their task of interest.

2017

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Proceedings of the 4th Workshop on Argument Mining
Ivan Habernal | Iryna Gurevych | Kevin Ashley | Claire Cardie | Nancy Green | Diane Litman | Georgios Petasis | Chris Reed | Noam Slonim | Vern Walker
Proceedings of the 4th Workshop on Argument Mining

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Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization
Roy Bar-Haim | Lilach Edelstein | Charles Jochim | Noam Slonim
Proceedings of the 4th Workshop on Argument Mining

Stance classification is a core component in on-demand argument construction pipelines. Previous work on claim stance classification relied on background knowledge such as manually-composed sentiment lexicons. We show that both accuracy and coverage can be significantly improved through automatic expansion of the initial lexicon. We also developed a set of contextual features that further improves the state-of-the-art for this task.

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Unsupervised corpus–wide claim detection
Ran Levy | Shai Gretz | Benjamin Sznajder | Shay Hummel | Ranit Aharonov | Noam Slonim
Proceedings of the 4th Workshop on Argument Mining

Automatic claim detection is a fundamental argument mining task that aims to automatically mine claims regarding a topic of consideration. Previous works on mining argumentative content have assumed that a set of relevant documents is given in advance. Here, we present a first corpus– wide claim detection framework, that can be directly applied to massive corpora. Using simple and intuitive empirical observations, we derive a claim sentence query by which we are able to directly retrieve sentences in which the prior probability to include topic-relevant claims is greatly enhanced. Next, we employ simple heuristics to rank the sentences, leading to an unsupervised corpus–wide claim detection system, with precision that outperforms previously reported results on the task of claim detection given relevant documents and labeled data.

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GRASP: Rich Patterns for Argumentation Mining
Eyal Shnarch | Ran Levy | Vikas Raykar | Noam Slonim
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

GRASP (GReedy Augmented Sequential Patterns) is an algorithm for automatically extracting patterns that characterize subtle linguistic phenomena. To that end, GRASP augments each term of input text with multiple layers of linguistic information. These different facets of the text terms are systematically combined to reveal rich patterns. We report highly promising experimental results in several challenging text analysis tasks within the field of Argumentation Mining. We believe that GRASP is general enough to be useful for other domains too. For example, each of the following sentences includes a claim for a [topic]: 1. Opponents often argue that the open primary is unconstitutional. [Open Primaries] 2. Prof. Smith suggested that affirmative action devalues the accomplishments of the chosen. [Affirmative Action] 3. The majority stated that the First Amendment does not guarantee the right to offend others. [Freedom of Speech] These sentences share almost no words in common, however, they are similar at a more abstract level. A human observer may notice the following underlying common structure, or pattern: [someone][argue/suggest/state][that][topic term][sentiment term]. GRASP aims to automatically capture such underlying structures of the given data. For the above examples it finds the pattern [noun][express][that][noun,topic][sentiment], where [express] stands for all its (in)direct hyponyms, and [noun,topic] means a noun which is also related to the topic.

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Stance Classification of Context-Dependent Claims
Roy Bar-Haim | Indrajit Bhattacharya | Francesco Dinuzzo | Amrita Saha | Noam Slonim
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Recent work has addressed the problem of detecting relevant claims for a given controversial topic. We introduce the complementary task of Claim Stance Classification, along with the first benchmark dataset for this task. We decompose this problem into: (a) open-domain target identification for topic and claim (b) sentiment classification for each target, and (c) open-domain contrast detection between the topic and the claim targets. Manual annotation of the dataset confirms the applicability and validity of our model. We describe an implementation of our model, focusing on a novel algorithm for contrast detection. Our approach achieves promising results, and is shown to outperform several baselines, which represent the common practice of applying a single, monolithic classifier for stance classification.

2016

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Expert Stance Graphs for Computational Argumentation
Orith Toledo-Ronen | Roy Bar-Haim | Noam Slonim
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

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Claim Synthesis via Predicate Recycling
Yonatan Bilu | Noam Slonim
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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NLP Approaches to Computational Argumentation
Noam Slonim | Iryna Gurevych | Chris Reed | Benno Stein
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

Argumentation and debating represent primary intellectual activities of the human mind. People in all societies argue and debate, not only to convince others of their own opinions but also in order to explore the differences between multiple perspectives and conceptualizations, and to learn from this exploration. The process of reaching a resolution on controversial topics typically does not follow a simple sequence of purely logical steps. Rather it involves a wide variety of complex and interwoven actions. Presumably, pros and cons are identified, considered, and weighed, via cognitive processes that often involve persuasion and emotions, which are inherently harder to formalize from a computational perspective.This wide range of conceptual capabilities and activities, have only in part been studied in fields like CL and NLP, and typically within relatively small sub-communities that overlap the ACL audience. The new field of Computational Argumentation has very recently seen significant expansion within the CL and NLP community as new techniques and datasets start to become available, allowing for the first time investigation of the computational aspects of human argumentation in a holistic manner.The main goal of this tutorial would be to introduce this rapidly evolving field to the CL community. Specifically, we will aim to review recent advances in the field and to outline the challenging research questions - that are most relevant to the ACL audience - that naturally arise when trying to model human argumentation.We will further emphasize the practical value of this line of study, by considering real-world CL and NLP applications that are expected to emerge from this research, and to impact various industries, including legal, finance, healthcare, media, and education, to name just a few examples.The first part of the tutorial will provide introduction to the basics of argumentation and rhetoric. Next, we will cover fundamental analysis tasks in Computational Argumentation, including argumentation mining, revealing argument relations, assessing arguments quality, stance classification, polarity analysis, and more. After the coffee break, we will first review existing resources and recently introduced benchmark data. In the following part we will cover basic synthesis tasks in Computational Argumentation, including the relation to NLG and dialogue systems, and the evolving area of Debate Technologies, defined as technologies developed directly to enhance, support, and engage with human debating. Finally, we will present relevant demos, review potential applications, and discuss the future of this emerging field.

2015

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Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection
Ruty Rinott | Lena Dankin | Carlos Alzate Perez | Mitesh M. Khapra | Ehud Aharoni | Noam Slonim
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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TR9856: A Multi-word Term Relatedness Benchmark
Ran Levy | Liat Ein-Dor | Shay Hummel | Ruty Rinott | Noam Slonim
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Automatic Claim Negation: Why, How and When
Yonatan Bilu | Daniel Hershcovich | Noam Slonim
Proceedings of the 2nd Workshop on Argumentation Mining

2014

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A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics
Ehud Aharoni | Anatoly Polnarov | Tamar Lavee | Daniel Hershcovich | Ran Levy | Ruty Rinott | Dan Gutfreund | Noam Slonim
Proceedings of the First Workshop on Argumentation Mining

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Context Dependent Claim Detection
Ran Levy | Yonatan Bilu | Daniel Hershcovich | Ehud Aharoni | Noam Slonim
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Claims on demand – an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora
Noam Slonim | Ehud Aharoni | Carlos Alzate | Roy Bar-Haim | Yonatan Bilu | Lena Dankin | Iris Eiron | Daniel Hershcovich | Shay Hummel | Mitesh Khapra | Tamar Lavee | Ran Levy | Paul Matchen | Anatoly Polnarov | Vikas Raykar | Ruty Rinott | Amrita Saha | Naama Zwerdling | David Konopnicki | Dan Gutfreund
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations

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