Ondřej Cífka


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Black-box language model explanation by context length probing
Ondřej Cífka | Antoine Liutkus
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

The increasingly widespread adoption of large language models has highlighted the need for improving their explainability. We present *context length probing*, a novel explanation technique for causal language models, based on tracking the predictions of a model as a function of the length of available context, and allowing to assign *differential importance scores* to different contexts. The technique is model-agnostic and does not rely on access to model internals beyond computing token-level probabilities. We apply context length probing to large pre-trained language models and offer some initial analyses and insights, including the potential for studying long-range dependencies. The [source code](https://github.com/cifkao/context-probing/) and an [interactive demo](https://cifkao.github.io/context-probing/) of the method are available.


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Are BLEU and Meaning Representation in Opposition?
Ondřej Cífka | Ondřej Bojar
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

One of possible ways of obtaining continuous-space sentence representations is by training neural machine translation (NMT) systems. The recent attention mechanism however removes the single point in the neural network from which the source sentence representation can be extracted. We propose several variations of the attentive NMT architecture bringing this meeting point back. Empirical evaluation suggests that the better the translation quality, the worse the learned sentence representations serve in a wide range of classification and similarity tasks.

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Neural Monkey: The Current State and Beyond
Jindřich Helcl | Jindřich Libovický | Tom Kocmi | Tomáš Musil | Ondřej Cífka | Dušan Variš | Ondřej Bojar
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)


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UFAL Submissions to the IWSLT 2016 MT Track
Ondřej Bojar | Ondřej Cífka | Jindřich Helcl | Tom Kocmi | Roman Sudarikov
Proceedings of the 13th International Conference on Spoken Language Translation

We present our submissions to the IWSLT 2016 machine translation task, as our first attempt to translate subtitles and one of our early experiments with neural machine translation (NMT). We focus primarily on English→Czech translation direction but perform also basic adaptation experiments for NMT with German and also the reverse direction. Three MT systems are tested: (1) our Chimera, a tight combination of phrase-based MT and deep linguistic processing, (2) Neural Monkey, our implementation of a NMT system in TensorFlow and (3) Nematus, an established NMT system.