Benjamin Heinzerling


2021

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Learning to Learn to be Right for the Right Reasons
Pride Kavumba | Benjamin Heinzerling | Ana Brassard | Kentaro Inui
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Improving model generalization on held-out data is one of the core objectives in common- sense reasoning. Recent work has shown that models trained on the dataset with superficial cues tend to perform well on the easy test set with superficial cues but perform poorly on the hard test set without superficial cues. Previous approaches have resorted to manual methods of encouraging models not to overfit to superficial cues. While some of the methods have improved performance on hard instances, they also lead to degraded performance on easy in- stances. Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and the hard test set without superficial cues. Using a meta-learning objective, we learn such a model that improves performance on both the easy test set and the hard test set. By evaluating our models on Choice of Plausible Alternatives (COPA) and Commonsense Explanation, we show that our proposed method leads to improved performance on both the easy test set and the hard test set upon which we observe up to 16.5 percentage points improvement over the baseline.

2019

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On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages
Yi Zhu | Benjamin Heinzerling | Ivan Vulić | Michael Strube | Roi Reichart | Anna Korhonen
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Recent work has validated the importance of subword information for word representation learning. Since subwords increase parameter sharing ability in neural models, their value should be even more pronounced in low-data regimes. In this work, we therefore provide a comprehensive analysis focused on the usefulness of subwords for word representation learning in truly low-resource scenarios and for three representative morphological tasks : fine-grained entity typing, morphological tagging, and named entity recognition. We conduct a systematic study that spans several dimensions of comparison : 1) type of data scarcity which can stem from the lack of task-specific training data, or even from the lack of unannotated data required to train word embeddings, or both ; 2) language type by working with a sample of 16 typologically diverse languages including some truly low-resource ones (e.g. Rusyn, Buryat, and Zulu) ; 3) the choice of the subword-informed word representation method. Our main results show that subword-informed models are universally useful across all language types, with large gains over subword-agnostic embeddings. They also suggest that the effective use of subwords largely depends on the language (type) and the task at hand, as well as on the amount of available data for training the embeddings and task-based models, where having sufficient in-task data is a more critical requirement.

2017

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Revisiting Selectional Preferences for Coreference Resolution
Benjamin Heinzerling | Nafise Sadat Moosavi | Michael Strube
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Selectional preferences have long been claimed to be essential for coreference resolution. However, they are modeled only implicitly by current coreference resolvers. We propose a dependency-based embedding model of selectional preferences which allows fine-grained compatibility judgments with high coverage. Incorporating our model improves performance, matching state-of-the-art results of a more complex system. However, it comes with a cost that makes it debatable how worthwhile are such improvements.

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Trust, but Verify ! Better Entity Linking through Automatic Verification
Benjamin Heinzerling | Michael Strube | Chin-Yew Lin
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We introduce automatic verification as a post-processing step for entity linking (EL). The proposed method trusts EL system results collectively, by assuming entity mentions are mostly linked correctly, in order to create a semantic profile of the given text using geospatial and temporal information, as well as fine-grained entity types. This profile is then used to automatically verify each linked mention individually, i.e., to predict whether it has been linked correctly or not. Verification allows leveraging a rich set of global and pairwise features that would be prohibitively expensive for EL systems employing global inference. Evaluation shows consistent improvements across datasets and systems. In particular, when applied to state-of-the-art systems, our method yields an absolute improvement in linking performance of up to 1.7 F1 on AIDA / CoNLL’03 and up to 2.4 F1 on the English TAC KBP 2015 TEDL dataset.