Alert button
Picture for Omer Goldman

Omer Goldman

Alert button

Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew

Nov 01, 2023
Eylon Gueta, Omer Goldman, Reut Tsarfaty

Pre-trained language models (PLMs) have shown remarkable successes in acquiring a wide range of linguistic knowledge, relying solely on self-supervised training on text streams. Nevertheless, the effectiveness of this language-agnostic approach has been frequently questioned for its sub-optimal performance when applied to morphologically-rich languages (MRLs). We investigate the hypothesis that incorporating explicit morphological knowledge in the pre-training phase can improve the performance of PLMs for MRLs. We propose various morphologically driven tokenization methods enabling the model to leverage morphological cues beyond raw text. We pre-train multiple language models utilizing the different methods and evaluate them on Hebrew, a language with complex and highly ambiguous morphology. Our experiments show that morphologically driven tokenization demonstrates improved results compared to a standard language-agnostic tokenization, on a benchmark of both semantic and morphologic tasks. These findings suggest that incorporating morphological knowledge holds the potential for further improving PLMs for morphologically rich languages.

Viaarxiv icon

Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces

Oct 24, 2023
Tal Levy, Omer Goldman, Reut Tsarfaty

Figure 1 for Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces
Figure 2 for Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces
Figure 3 for Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces
Figure 4 for Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces

The ability to identify and control different kinds of linguistic information encoded in vector representations of words has many use cases, especially for explainability and bias removal. This is usually done via a set of simple classification tasks, termed probes, to evaluate the information encoded in the embedding space. However, the involvement of a trainable classifier leads to entanglement between the probe's results and the classifier's nature. As a result, contemporary works on probing include tasks that do not involve training of auxiliary models. In this work we introduce the term indicator tasks for non-trainable tasks which are used to query embedding spaces for the existence of certain properties, and claim that this kind of tasks may point to a direction opposite to probes, and that this contradiction complicates the decision on whether a property exists in an embedding space. We demonstrate our claims with two test cases, one dealing with gender debiasing and another with the erasure of morphological information from embedding spaces. We show that the application of a suitable indicator provides a more accurate picture of the information captured and removed compared to probes. We thus conclude that indicator tasks should be implemented and taken into consideration when eliciting information from embedded representations.

* Findings of EMNLP 2023 
Viaarxiv icon

The Curious Case of Hallucinatory Unanswerablity: Finding Truths in the Hidden States of Over-Confident Large Language Models

Oct 18, 2023
Aviv Slobodkin, Omer Goldman, Avi Caciularu, Ido Dagan, Shauli Ravfogel

Large language models (LLMs) have been shown to possess impressive capabilities, while also raising crucial concerns about the faithfulness of their responses. A primary issue arising in this context is the management of unanswerable queries by LLMs, which often results in hallucinatory behavior, due to overconfidence. In this paper, we explore the behavior of LLMs when presented with unanswerable queries. We ask: do models \textbf{represent} the fact that the question is unanswerable when generating a hallucinatory answer? Our results show strong indications that such models encode the answerability of an input query, with the representation of the first decoded token often being a strong indicator. These findings shed new light on the spatial organization within the latent representations of LLMs, unveiling previously unexplored facets of these models. Moreover, they pave the way for the development of improved decoding techniques with better adherence to factual generation, particularly in scenarios where query unanswerability is a concern.

* EMNLP 2023 
Viaarxiv icon

Morphological Inflection with Phonological Features

Jun 21, 2023
David Guriel, Omer Goldman, Reut Tsarfaty

Figure 1 for Morphological Inflection with Phonological Features
Figure 2 for Morphological Inflection with Phonological Features
Figure 3 for Morphological Inflection with Phonological Features
Figure 4 for Morphological Inflection with Phonological Features

Recent years have brought great advances into solving morphological tasks, mostly due to powerful neural models applied to various tasks as (re)inflection and analysis. Yet, such morphological tasks cannot be considered solved, especially when little training data is available or when generalizing to previously unseen lemmas. This work explores effects on performance obtained through various ways in which morphological models get access to subcharacter phonological features that are the targets of morphological processes. We design two methods to achieve this goal: one that leaves models as is but manipulates the data to include features instead of characters, and another that manipulates models to take phonological features into account when building representations for phonemes. We elicit phonemic data from standard graphemic data using language-specific grammars for languages with shallow grapheme-to-phoneme mapping, and we experiment with two reinflection models over eight languages. Our results show that our methods yield comparable results to the grapheme-based baseline overall, with minor improvements in some of the languages. All in all, we conclude that patterns in character distributions are likely to allow models to infer the underlying phonological characteristics, even when phonemes are not explicitly represented.

* ACL 2023 main conference; 8 pages, 1 figure 
Viaarxiv icon

Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks

May 17, 2023
Alon Jacovi, Avi Caciularu, Omer Goldman, Yoav Goldberg

Data contamination has become especially prevalent and challenging with the rise of models pretrained on very large, automatically-crawled corpora. For closed models, the training data becomes a trade secret, and even for open models, it is not trivial to ascertain whether a particular test instance has been compromised. Strategies such as live leaderboards with hidden answers, or using test data which is guaranteed to be unseen, are expensive and become fragile with time. Assuming that all relevant actors value clean test data and will cooperate to mitigate data contamination, what can be done? We propose three strategies that can make a difference: (1) Test data made public should be encrypted with a public key and licensed to disallow derivative distribution; (2) demand training exclusion controls from closed API holders, and protect your test data by refusing to evaluate until demands are met; (3) in case of test data based on internet text, avoid data which appears with its solution on the internet, and release the context of internet-derived data along with the data. These strategies are practical and can be effective in preventing data contamination and allowing trustworthy evaluation of models' capabilities.

Viaarxiv icon

UniMorph 4.0: Universal Morphology

May 10, 2022
Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina J. Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud'hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova

Figure 1 for UniMorph 4.0: Universal Morphology
Figure 2 for UniMorph 4.0: Universal Morphology
Figure 3 for UniMorph 4.0: Universal Morphology
Figure 4 for UniMorph 4.0: Universal Morphology

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

* LREC 2022; The first two authors made equal contributions 
Viaarxiv icon

Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study

Mar 20, 2022
David Guriel, Omer Goldman, Reut Tsarfaty

Figure 1 for Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study
Figure 2 for Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study
Figure 3 for Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study
Figure 4 for Morphological Reinflection with Multiple Arguments: An Extended Annotation schema and a Georgian Case Study

In recent years, a flurry of morphological datasets had emerged, most notably UniMorph, a multi-lingual repository of inflection tables. However, the flat structure of the current morphological annotation schema makes the treatment of some languages quirky, if not impossible, specifically in cases of polypersonal agreement, where verbs agree with multiple arguments using true affixes. In this paper, we propose to address this phenomenon by expanding the UniMorph annotation schema to a hierarchical feature structure that naturally accommodates complex argument marking. We apply this extended schema to one such language, Georgian, and provide a human-verified, accurate and balanced morphological dataset for Georgian verbs. The dataset has 4 times more tables and 6 times more verb forms compared to the existing UniMorph dataset, covering all possible variants of argument marking, demonstrating the adequacy of our proposed scheme. Experiments with a standard reinflection model show that generalization is easy when the data is split at the form level, but extremely hard when splitting along lemma lines. Expanding the other languages in UniMorph to this schema is expected to improve both the coverage, consistency and interpretability of this benchmark.

* ACL 2022 
Viaarxiv icon

Morphology Without Borders: Clause-Level Morphological Annotation

Feb 25, 2022
Omer Goldman, Reut Tsarfaty

Figure 1 for Morphology Without Borders: Clause-Level Morphological Annotation
Figure 2 for Morphology Without Borders: Clause-Level Morphological Annotation
Figure 3 for Morphology Without Borders: Clause-Level Morphological Annotation
Figure 4 for Morphology Without Borders: Clause-Level Morphological Annotation

Morphological tasks use large multi-lingual datasets that organize words into inflection tables, which then serve as training and evaluation data for various tasks. However, a closer inspection of these data reveals profound cross-linguistic inconsistencies, that arise from the lack of a clear linguistic and operational definition of what is a word, and that severely impair the universality of the derived tasks. To overcome this deficiency, we propose to view morphology as a clause-level phenomenon, rather than word-level. It is anchored in a fixed yet inclusive set of features homogeneous across languages, that encapsulates all functions realized in a saturated clause. We deliver MightyMorph, a novel dataset for clause-level morphology covering 4 typologically-different languages: English, German, Turkish and Hebrew. We use this dataset to derive 3 clause-level morphological tasks: inflection, reinflection and analysis. Our experiments show that the clause-level tasks are substantially harder than the respective word-level tasks, while having comparable complexity across languages. Furthermore, redefining morphology to the clause-level provides a neat interface with contextualized language models (LMs) and can be used to probe LMs capacity to encode complex morphology. Taken together, this work opens up new horizons in the study of computational morphology, leaving ample space for studying neural morphological modeling cross-linguistically.

Viaarxiv icon

(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance

Aug 12, 2021
Omer Goldman, David Guriel, Reut Tsarfaty

Figure 1 for (Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance
Figure 2 for (Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance
Figure 3 for (Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance
Figure 4 for (Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance

In the domain of Morphology, Inflection is a fundamental and important task that gained a lot of traction in recent years, mostly via SIGMORPHON's shared-tasks. With average accuracy above 0.9 over the scores of all languages, the task is considered mostly solved using relatively generic neural sequence-to-sequence models, even with little data provided. In this work, we propose to re-evaluate morphological inflection models by employing harder train-test splits that will challenge the generalization capacity of the models. In particular, as opposed to the na\"ive split-by-form, we propose a split-by-lemma method to challenge the performance on existing benchmarks. Our experiments with the three top-ranked systems on the SIGMORPHON's 2020 shared-task show that the lemma-split presents an average drop of 30 percentage points in macro-average for the 90 languages included. The effect is most significant for low-resourced languages with a drop as high as 95 points, but even high-resourced languages lose about 10 points on average. Our results clearly show that generalizing inflection to unseen lemmas is far from being solved, presenting a simple yet effective means to promote more sophisticated models.

Viaarxiv icon

Minimal Supervision for Morphological Inflection

Apr 17, 2021
Omer Goldman, Reut Tsarfaty

Figure 1 for Minimal Supervision for Morphological Inflection
Figure 2 for Minimal Supervision for Morphological Inflection
Figure 3 for Minimal Supervision for Morphological Inflection
Figure 4 for Minimal Supervision for Morphological Inflection

Neural models for the various flavours of morphological inflection tasks have proven to be extremely accurate given ample labeled data -- data that may be slow and costly to obtain. In this work we aim to overcome this annotation bottleneck by bootstrapping labeled data from a seed as little as {\em five} labeled paradigms, accompanied by a large bulk of unlabeled text. Our approach exploits different kinds of regularities in morphological systems in a two-phased setup, where word tagging based on {\em analogies} is followed by word pairing based on {\em distances}. We experiment with the Paradigm Cell Filling Problem over eight typologically different languages, and find that, in languages with relatively simple morphology, orthographic regularities on their own allow inflection models to achieve respectable accuracy. Combined orthographic and semantic regularities alleviate difficulties with particularly complex morpho-phonological systems. Our results suggest that hand-crafting many tagged examples might be an unnecessary effort. However, more work is needed in order to address rarely used forms.

Viaarxiv icon