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Researcher profile

Amir Zeldes

Amir Zeldes contributes to research discovery and scholarly infrastructure.

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Computation and Language

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Published work

4 published item(s)

preprint2026arXiv

DiscoExplorer: An Open Interface for the Study of Multilingual Discourse Relations

The relations connecting propositions in discourse such as cause (A because B) or concession (A although B) are a subject of intense interest in Computational Linguistics and Pragmatics, but challenging to study and compare across languages. Recent progress in standardizing discourse relation inventories across datasets offers the potential to facilitate such studies, but is hindered by the complexity of relevant data and the lack of easily accessible interfaces to analyze it. In this paper we present DiscoExplorer, a new open source web interface, capable of running on local computers, which we use to make datasets from the DISRPT Shared Task on discourse relation classification publicly available, covering 16 different languages. We present the query language, search and visualization facilities for relations and signaling devices such as connectives, as well as some example studies.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

MicroBERT: Effective Training of Low-resource Monolingual BERTs through Parameter Reduction and Multitask Learning

Transformer language models (TLMs) are critical for most NLP tasks, but they are difficult to create for low-resource languages because of how much pretraining data they require. In this work, we investigate two techniques for training monolingual TLMs in a low-resource setting: greatly reducing TLM size, and complementing the masked language modeling objective with two linguistically rich supervised tasks (part-of-speech tagging and dependency parsing). Results from 7 diverse languages indicate that our model, MicroBERT, is able to produce marked improvements in downstream task evaluations relative to a typical monolingual TLM pretraining approach. Specifically, we find that monolingual MicroBERT models achieve gains of up to 18% for parser LAS and 11% for NER F1 compared to a multilingual baseline, mBERT, while having less than 1% of its parameter count. We conclude reducing TLM parameter count and using labeled data for pretraining low-resource TLMs can yield large quality benefits and in some cases produce models that outperform multilingual approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Cross-Genre Ensemble Approach to Robust Reddit Part of Speech Tagging

Part of speech tagging is a fundamental NLP task often regarded as solved for high-resource languages such as English. Current state-of-the-art models have achieved high accuracy, especially on the news domain. However, when these models are applied to other corpora with different genres, and especially user-generated data from the Web, we see substantial drops in performance. In this work, we study how a state-of-the-art tagging model trained on different genres performs on Web content from unfiltered Reddit forum discussions. More specifically, we use data from multiple sources: OntoNotes, a large benchmark corpus with 'well-edited' text, the English Web Treebank with 5 Web genres, and GUM, with 7 further genres other than Reddit. We report the results when training on different splits of the data, tested on Reddit. Our results show that even small amounts of in-domain data can outperform the contribution of data an order of magnitude larger coming from other Web domains. To make progress on out-of-domain tagging, we also evaluate an ensemble approach using multiple single-genre taggers as input features to a meta-classifier. We present state of the art performance on tagging Reddit data, as well as error analysis of the results of these models, and offer a typology of the most common error types among them, broken down by training corpus.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

AMALGUM -- A Free, Balanced, Multilayer English Web Corpus

We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory. By tapping open online data sources the corpus is meant to offer a more sizable alternative to smaller manually created annotated data sets, while avoiding pitfalls such as imbalanced or unknown composition, licensing problems, and low-quality natural language processing. We harness knowledge from multiple annotation layers in order to achieve a "better than NLP" benchmark and evaluate the accuracy of the resulting resource.

0 citations0 reviewsNo code linkedOpen access