Orith Toledo-Ronen


2022

pdf bib
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.

2021

pdf bib
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.

2020

pdf bib
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.

2019

pdf bib
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.

pdf bib
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.

2018

pdf bib
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.

2016

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