Tobias Schnabel


2022

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SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
Philippe Laban | Tobias Schnabel | Paul N. Bennett | Marti A. Hearst
Transactions of the Association for Computational Linguistics, Volume 10

In the summarization domain, a key requirement for summaries is to be factually consistent with the input document. Previous work has found that natural language inference (NLI) models do not perform competitively when applied to inconsistency detection. In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level). We provide a highly effective and light-weight method called SummaCConv that enables NLI models to be successfully used for this task by segmenting documents into sentence units and aggregating scores between pairs of sentences. We furthermore introduce a new benchmark called SummaC (Summary Consistency) which consists of six large inconsistency detection datasets. On this dataset, SummaCConv obtains state-of-the-art results with a balanced accuracy of 74.4%, a 5% improvement compared with prior work.

2021

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Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text
Philippe Laban | Tobias Schnabel | Paul Bennett | Marti A. Hearst
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity. We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate’s reward, and encourages candidates that outperform the mean reward. Finally, we propose a realistic text comprehension task as an evaluation method for text simplification. When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18% faster while retaining accuracy, when compared to the original text.

2015

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Evaluation methods for unsupervised word embeddings
Tobias Schnabel | Igor Labutov | David Mimno | Thorsten Joachims
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Online Updating of Word Representations for Part-of-Speech Tagging
Wenpeng Yin | Tobias Schnabel | Hinrich Schütze
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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FLORS: Fast and Simple Domain Adaptation for Part-of-Speech Tagging
Tobias Schnabel | Hinrich Schütze
Transactions of the Association for Computational Linguistics, Volume 2

We present FLORS, a new part-of-speech tagger for domain adaptation. FLORS uses robust representations that work especially well for unknown words and for known words with unseen tags. FLORS is simpler and faster than previous domain adaptation methods, yet it has significantly better accuracy than several baselines.

2013

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Towards Robust Cross-Domain Domain Adaptation for Part-of-Speech Tagging
Tobias Schnabel | Hinrich Schütze
Proceedings of the Sixth International Joint Conference on Natural Language Processing