Large margin filtering for signal sequence labeling

Signal Sequence Labeling consists in predicting a sequence of labels given an observed sequence of samples. A naive way is to filter the signal in order to reduce the noise and to apply a classification algorithm on the filtered samples. We propose in this paper to jointly learn the filter with the classifier leading to a large margin filtering for classification. This method allows to learn the optimal cutoff frequency and phase of the filter that may be different from zero. Two methods are proposed and tested on a toy dataset and on a real life BCI dataset from BCI Competition III.Comment: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010, Dallas : United States (2010

Learning Dynamic Feature Selection for Fast Sequential Prediction

We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the heart of many NLP components. This is accomplished by partitioning the features into a sequence of templates which are ordered such that high confidence can often be reached using only a small fraction of all features. Parameter estimation is arranged to maximize accuracy and early confidence in this sequence. Our approach is simpler and better suited to NLP than other related cascade methods. We present experiments in left-to-right part-of-speech tagging, named entity recognition, and transition-based dependency parsing. On the typical benchmarking datasets we can preserve POS tagging accuracy above 97% and parsing LAS above 88.5% both with over a five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase in speed.Comment: Appears in The 53rd Annual Meeting of the Association for Computational Linguistics, Beijing, China, July 201

Uncertainty Detection as Approximate Max-Margin Sequence Labelling

This paper reports experiments for the CoNLL 2010 shared task on learning to detect hedges and their scope in natural language text. We have addressed the experimental tasks as supervised linear maximum margin prediction problems. For sentence level hedge detection in the biological domain we use an L1-regularised binary support vector machine, while for sentence level weasel detection in the Wikipedia domain, we use an L2-regularised approach. We model the in-sentence uncertainty cue and scope detection task as an L2-regularised approximate maximum margin sequence labelling problem, using the BIO-encoding. In addition to surface level features, we use a variety of linguistic features based on a functional dependency analysis. A greedy forward selection strategy is used in exploring the large set of potential features. Our official results for Task 1 for the biological domain are 85.2 F1-score, for the Wikipedia set 55.4 F1-score. For Task 2, our official results are 2.1 for the entire task with a score of 62.5 for cue detection. After resolving errors and final bugs, our final results are for Task 1, biological: 86.0, Wikipedia: 58.2; Task 2, scopes: 39.6 and cues: 78.5

Classical Structured Prediction Losses for Sequence to Sequence Learning

There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models. Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup. We also report new state of the art results on both IWSLT'14 German-English translation as well as Gigaword abstractive summarization. On the larger WMT'14 English-French translation task, sequence-level training achieves 41.5 BLEU which is on par with the state of the art.Comment: 10 pages, NAACL 201