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Liat Ein-Dor

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3 papers

2 author rows

AAAI Conference 2022 Conference Paper

Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis

Liat Ein-Dor
Ilya Shnayderman
Artem Spector
Lena Dankin
Ranit Aharonov
Noam Slonim

In recent years, pretrained language models have revolutionized the NLP world, while achieving state-of-the-art performance in various downstream tasks. However, in many cases, these models do not perform well when labeled data is scarce and the model is expected to perform in the zero or few shot setting. Recently, several works have shown that continual pretraining or performing a second phase of pretraining (inter-training), which is better aligned with the downstream task, can lead to improved results, especially in the scarce data setting. Here, we propose to leverage sentiment-carrying discoursemarkers to generate large-scale weakly-labeled data, which in turn can be used to adapt general-purpose language models to the task of sentiment classification. In addition, we propose a new method for adapting sentiment classification models to new domains. This method is based on automatic identification of domain-specific sentiment-carrying discourse markers. Extensive experimental results show the value of our approach on various benchmark datasets. Code, models and data are available at https: //github. com/ibm/tslm-discourse-markers.

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AAAI Conference 2020 Conference Paper

Corpus Wide Argument Mining—A Working Solution

Liat Ein-Dor
Eyal Shnarch
Lena Dankin
Alon Halfon
Benjamin Sznajder
Ariel Gera
Carlos Alzate
Martin Gleize

One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a ﬁrst end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates.

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UAI Conference 2007 Conference Paper

Ranking Under Uncertainty

Or Zuk
Liat Ein-Dor
Eytan Domany

Ranking objects is a simple and natural pro- cedure for organizing data. It is often per- formed by assigning a quality score to each object according to its relevance to the prob- lem at hand. Ranking is widely used for ob- ject selection, when resources are limited and it is necessary to select a subset of most rel- evant objects for further processing. In real world situations, the object's scores are often calculated from noisy measurements, casting doubt on the ranking reliability. We intro- duce an analytical method for assessing the influence of noise levels on the ranking re- liability. We use two similarity measures for reliability evaluation, Top-K-List overlap and Kendall's tau measure, and show that the for- mer is much more sensitive to noise than the latter. We apply our method to gene selec- tion in a series of microarray experiments of several cancer types. The results indicate that the reliability of the lists obtained from these experiments is very poor, and that ex- periment sizes which are necessary for attain- ing reasonably stable Top-K-Lists are much larger than those currently available. Simu- lations support our analytical results.

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