Chris Hokamp

Also published as: Christopher Hokamp


2020

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A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
Demian Gholipour Ghalandari | Chris Hokamp | Nghia The Pham | John Glover | Georgiana Ifrim
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation. However, there is a lack of datasets that realistically address such use cases at a scale large enough for training supervised models for this task. This work presents a new dataset for MDS that is large both in the total number of document clusters and in the size of individual clusters. We build this dataset by leveraging the Wikipedia Current Events Portal (WCEP), which provides concise and neutral human-written summaries of news events, with links to external source articles. We also automatically extend these source articles by looking for related articles in the Common Crawl archive. We provide a quantitative analysis of the dataset and empirical results for several state-of-the-art MDS techniques.

2019

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Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models
Chris Hokamp | John Glover | Demian Gholipour Ghalandari
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

We study several methods for full or partial sharing of the decoder parameters of multi-lingual NMT models. Using only the WMT 2019 shared task parallel datasets for training, we evaluate both fully supervised and zero-shot translation performance in 110 unique translation directions. We use additional test sets and re-purpose evaluation methods recently used for unsupervised MT in order to evaluate zero-shot translation performance for language pairs where no gold-standard parallel data is available. To our knowledge, this is the largest evaluation of multi-lingual translation yet conducted in terms of the total size of the training data we use, and in terms of the number of zero-shot translation pairs we evaluate. We conduct an in-depth evaluation of the translation performance of different models, highlighting the trade-offs between methods of sharing decoder parameters. We find that models which have task-specific decoder parameters outperform models where decoder parameters are fully shared across all tasks.

2018

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Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models
Henry Elder | Chris Hokamp
Proceedings of the First Workshop on Multilingual Surface Realisation

This work presents state of the art results in reconstruction of surface realizations from obfuscated text. We identify the lack of sufficient training data as the major obstacle to training high-performing models, and solve this issue by generating large amounts of synthetic training data. We also propose preprocessing techniques which make the structure contained in the input features more accessible to sequence models. Our models were ranked first on all evaluation metrics in the English portion of the 2018 Surface Realization shared task.

2017

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Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search
Chris Hokamp | Qun Liu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model which generates sequences token by token. Lexical constraints take the form of phrases or words that must be present in the output sequence. This is a very general way to incorporate auxillary knowledge into a model’s output without requiring any modification of the parameters or training data. We demonstrate the feasibility and flexibility of Lexically Constrained Decoding by conducting experiments on Neural Interactive-Predictive Translation, as well as Domain Adaptation for Neural Machine Translation. Experiments show that GBS can provide large improvements in translation quality in interactive scenarios, and that, even without any user input, GBS can be used to achieve significant gains in performance in domain adaptation scenarios.

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Pushing the Limits of Translation Quality Estimation
André F. T. Martins | Marcin Junczys-Dowmunt | Fabio N. Kepler | Ramón Astudillo | Chris Hokamp | Roman Grundkiewicz
Transactions of the Association for Computational Linguistics, Volume 5

Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways. However, this potential is currently limited by the relatively low accuracy of existing systems. In this paper, we achieve remarkable improvements by exploiting synergies between the related tasks of word-level quality estimation and automatic post-editing. First, we stack a new, carefully engineered, neural model into a rich feature-based word-level quality estimation system. Then, we use the output of an automatic post-editing system as an extra feature, obtaining striking results on WMT16: a word-level FMULT1 score of 57.47% (an absolute gain of +7.95% over the current state of the art), and a Pearson correlation score of 65.56% for sentence-level HTER prediction (an absolute gain of +13.36%).

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Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation
Chris Hokamp
Proceedings of the Second Conference on Machine Translation

2016

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MARMOT: A Toolkit for Translation Quality Estimation at the Word Level
Varvara Logacheva | Chris Hokamp | Lucia Specia
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present Marmot~― a new toolkit for quality estimation (QE) of machine translation output. Marmot contains utilities targeted at quality estimation at the word and phrase level. However, due to its flexibility and modularity, it can also be extended to work at the sentence level. In addition, it can be used as a framework for extracting features and learning models for many common natural language processing tasks. The tool has a set of state-of-the-art features for QE, and new features can easily be added. The tool is open-source and can be downloaded from https://github.com/qe-team/marmot/

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Unbabel’s Participation in the WMT16 Word-Level Translation Quality Estimation Shared Task
André F. T. Martins | Ramón Astudillo | Chris Hokamp | Fabio Kepler
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Improving Phrase-Based SMT Using Cross-Granularity Embedding Similarity
Peyman Passban | Chris Hokamp | Andy Way | Qun Liu
Proceedings of the 19th Annual Conference of the European Association for Machine Translation

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DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity
Chris Hokamp | Piyush Arora
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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The DCU Discourse Parser for Connective, Argument Identification and Explicit Sense Classification
Longyue Wang | Chris Hokamp | Tsuyoshi Okita | Xiaojun Zhang | Qun Liu
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

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HandyCAT - An Open-Source Platform for CAT Tool Research
Chris Hokamp | Qun Liu
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

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Proceedings of the Tenth Workshop on Statistical Machine Translation
Ondřej Bojar | Rajan Chatterjee | Christian Federmann | Barry Haddow | Chris Hokamp | Matthias Huck | Varvara Logacheva | Pavel Pecina
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Findings of the 2015 Workshop on Statistical Machine Translation
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Barry Haddow | Matthias Huck | Chris Hokamp | Philipp Koehn | Varvara Logacheva | Christof Monz | Matteo Negri | Matt Post | Carolina Scarton | Lucia Specia | Marco Turchi
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Data enhancement and selection strategies for the word-level Quality Estimation
Varvara Logacheva | Chris Hokamp | Lucia Specia
Proceedings of the Tenth Workshop on Statistical Machine Translation

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HandyCAT - An Open-Source Platform for CAT Tool Research
Christopher Hokamp | Qun Liu
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

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Using Word Semantics To Assist English as a Second Language Learners
Mahmoud Azab | Chris Hokamp | Rada Mihalcea
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2
Piyush Arora | Chris Hokamp | Jennifer Foster | Gareth Jones
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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Target-Centric Features for Translation Quality Estimation
Chris Hokamp | Iacer Calixto | Joachim Wagner | Jian Zhang
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Modeling Language Proficiency Using Implicit Feedback
Chris Hokamp | Rada Mihalcea | Peter Schuelke
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We describe the results of several experiments with interactive interfaces for native and L2 English students, designed to collect implicit feedback from students as they complete a reading activity. In this study, implicit means that all data is obtained without asking the user for feedback. To test the value of implicit feedback for assessing student proficiency, we collect features of user behavior and interaction, which are then used to train classification models. Based upon the feedback collected during these experiments, a student’s performance on a quiz and proficiency relative to other students can be accurately predicted, which is a step on the path to our goal of providing automatic feedback and unintrusive evaluation in interactive learning environments.

2013

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Sense Clustering Using Wikipedia
Bharath Dandala | Chris Hokamp | Rada Mihalcea | Razvan Bunescu
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013