Shira Wein


2023

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Follow the leader(board) with confidence: Estimating p-values from a single test set with item and response variance
Shira Wein | Christopher Homan | Lora Aroyo | Chris Welty
Findings of the Association for Computational Linguistics: ACL 2023

Among the problems with leaderboard culture in NLP has been the widespread lack of confidence estimation in reported results. In this work, we present a framework and simulator for estimating p-values for comparisons between the results of two systems, in order to understand the confidence that one is actually better (i.e. ranked higher) than the other. What has made this difficult in the past is that each system must itself be evaluated by comparison to a gold standard. We define a null hypothesis that each system’s metric scores are drawn from the same distribution, using variance found naturally (though rarely reported) in test set items and individual labels on an item (responses) to produce the metric distributions. We create a test set that evenly mixes the responses of the two systems under the assumption the null hypothesis is true. Exploring how to best estimate the true p-value from a single test set under different metrics, tests, and sampling methods, we find that the presence of response variance (from multiple raters or multiple model versions) has a profound impact on p-value estimates for model comparison, and that choice of metric and sampling method is critical to providing statistical guarantees on model comparisons.

2022

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Accounting for Language Effect in the Evaluation of Cross-lingual AMR Parsers
Shira Wein | Nathan Schneider
Proceedings of the 29th International Conference on Computational Linguistics

Cross-lingual Abstract Meaning Representation (AMR) parsers are currently evaluated in comparison to gold English AMRs, despite parsing a language other than English, due to the lack of multilingual AMR evaluation metrics. This evaluation practice is problematic because of the established effect of source language on AMR structure. In this work, we present three multilingual adaptations of monolingual AMR evaluation metrics and compare the performance of these metrics to sentence-level human judgments. We then use our most highly correlated metric to evaluate the output of state-of-the-art cross-lingual AMR parsers, finding that Smatch may still be a useful metric in comparison to gold English AMRs, while our multilingual adaptation of S2match (XS2match) is best for comparison with gold in-language AMRs.

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Crowdsourcing Preposition Sense Disambiguation with High Precision via a Priming Task
Shira Wein | Nathan Schneider
Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)

The careful design of a crowdsourcing protocol is critical to eliciting highly accurate annotations from untrained workers. In this work, we explore the development of crowdsourcing protocols for a challenging word sense disambiguation task. We find that (a) selecting a similar example usage can serve as a proxy for selecting an explicit definition of the sense, and (b) priming workers with an additional, related task within the HIT improves performance on the main proxy task. Ultimately, we demonstrate the usefulness of our crowdsourcing elicitation technique as an effective alternative to previously investigated training strategies, which can be used if agreement on a challenging task is low.

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Spanish Abstract Meaning Representation: Annotation of a General Corpus
Shira Wein | Lucia Donatelli | Ethan Ricker | Calvin Engstrom | Alex Nelson | Leonie Harter | Nathan Schneider
Northern European Journal of Language Technology, Volume 8

Abstract Meaning Representation (AMR), originally designed for English, has been adapted to a number of languages to facilitate cross-lingual semantic representation and analysis. We build on previous work and present the first sizable, general annotation project for Spanish AMR. We release a detailed set of annotation guidelines and a corpus of 486 gold-annotated sentences spanning multiple genres from an existing, cross-lingual AMR corpus. Our work constitutes the second largest non-English gold AMR corpus to date. Fine-tuning an AMR to-Spanish generation model with our annotations results in a BERTScore improvement of 8.8%, demonstrating initial utility of our work.

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Effect of Source Language on AMR Structure
Shira Wein | Wai Ching Leung | Yifu Mu | Nathan Schneider
Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022

The Abstract Meaning Representation (AMR) annotation schema was originally designed for English. But the formalism has since been adapted for annotation in a variety of languages. Meanwhile, cross-lingual parsers have been developed to derive English AMR representations for sentences from other languages—implicitly assuming that English AMR can approximate an interlingua. In this work, we investigate the similarity of AMR annotations in parallel data and how much the language matters in terms of the graph structure. We set out to quantify the effect of sentence language on the structure of the parsed AMR. As a case study, we take parallel AMR annotations from Mandarin Chinese and English AMRs, and replace all Chinese concepts with equivalent English tokens. We then compare the two graphs via the Smatch metric as a measure of structural similarity. We find that source language has a dramatic impact on AMR structure, with Smatch scores below 50% between English and Chinese graphs in our sample—an important reference point for interpreting Smatch scores in cross-lingual AMR parsing.

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Semantic Similarity as a Window into Vector- and Graph-Based Metrics
Wai Ching Leung | Shira Wein | Nathan Schneider
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

In this work, we use sentence similarity as a lens through which to investigate the representation of meaning in graphs vs. vectors. On semantic textual similarity data, we examine how similarity metrics based on vectors alone (SENTENCE-BERT and BERTSCORE) fare compared to metrics based on AMR graphs (SMATCH and S2MATCH). Quantitative and qualitative analyses show that the AMR-based metrics can better capture meanings dependent on sentence structures, but can also be distracted by structural differences—whereas the BERT-based metrics represent finer-grained meanings of individual words, but often fail to capture the ordering effect of words within sentences and suffer from interpretability problems. These findings contribute to our understanding of each approach to semantic representation and motivate distinct use cases for graph and vector-based representations.

2021

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Supersense and Sensibility: Proxy Tasks for Semantic Annotation of Prepositions
Luke Gessler | Shira Wein | Nathan Schneider
Proceedings of the Society for Computation in Linguistics 2021

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Classifying Divergences in Cross-lingual AMR Pairs
Shira Wein | Nathan Schneider
Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop

Translation divergences are varied and widespread, challenging approaches that rely on parallel text. To annotate translation divergences, we propose a schema grounded in the Abstract Meaning Representation (AMR), a sentence-level semantic framework instantiated for a number of languages. By comparing parallel AMR graphs, we can identify specific points of divergence. Each divergence is labeled with both a type and a cause. We release a small corpus of annotated English-Spanish data, and analyze the annotations in our corpus.

2020

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A Human Evaluation of AMR-to-English Generation Systems
Emma Manning | Shira Wein | Nathan Schneider
Proceedings of the 28th International Conference on Computational Linguistics

Most current state-of-the art systems for generating English text from Abstract Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic for natural language generation. In this work, we present the results of a new human evaluation which collects fluency and adequacy scores, as well as categorization of error types, for several recent AMR generation systems. We discuss the relative quality of these systems and how our results compare to those of automatic metrics, finding that while the metrics are mostly successful in ranking systems overall, collecting human judgments allows for more nuanced comparisons. We also analyze common errors made by these systems.

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PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English
Michael Kranzlein | Emma Manning | Siyao Peng | Shira Wein | Aryaman Arora | Nathan Schneider
Proceedings of the 14th Linguistic Annotation Workshop

We present the Prepositions Annotated with Supsersense Tags in Reddit International English (“PASTRIE”) corpus, a new dataset containing manually annotated preposition supersenses of English data from presumed speakers of four L1s: English, French, German, and Spanish. The annotations are comprehensive, covering all preposition types and tokens in the sample. Along with the corpus, we provide analysis of distributional patterns across the included L1s and a discussion of the influence of L1s on L2 preposition choice.

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Supersense and Sensibility: Proxy Tasks for Semantic Annotation of Prepositions
Luke Gessler | Shira Wein | Nathan Schneider
Proceedings of the 14th Linguistic Annotation Workshop

Prepositional supersense annotation is time-consuming and requires expert training. Here, we present two sensible methods for obtaining prepositional supersense annotations indirectly by eliciting surface substitution and similarity judgments. Four pilot studies suggest that both methods have potential for producing prepositional supersense annotations that are comparable in quality to expert annotations.


Classification and Analysis of Neologisms Produced by Learners of Spanish: Effects of Proficiency and Task
Shira Wein
Proceedings of the The Fourth Widening Natural Language Processing Workshop

The Spanish Learner Language Oral Corpora (SPLLOC) of transcribed conversations between investigators and language learners contains a set of neologism tags. In this work, the utterances tagged as neologisms are broken down into three categories: true neologisms, loanwords, and errors. This work examines the relationships between neologism, loanword, and error production and both language learner level and conversation task. The results of this study suggest that loanwords and errors are produced most frequently by language learners with moderate experience, while neologisms are produced most frequently by native speakers. This study also indicates that tasks that require descriptions of images draw more neologism, loanword and error production. We ultimately present a unique analysis of the implications of neologism, loanword, and error production useful for further work in second language acquisition research, as well as for language educators.