Papers by Andy Way
FooTweets: A Bilingual Parallel Corpus of World Cup Tweets (L18-1)
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| Challenge: | a new study analyzes the nature of twitter data and compares it with other social networking websites. |
| Approach: | They develop a parallel corpus of tweets for an English-German pair that can be translated into German using a machine translation tool. |
| Outcome: | The proposed method can be used to translate tweets from English to German using a parallel corpus of 4, 000 tweets. |
Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism (D18-1)
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| Challenge: | Pronouns are often omitted in pro-drop languages, such as Chinese . this leads to various translation problems in terms of completeness, syntax and semantics . |
| Approach: | They propose a reconstruction-based approach to alleviate dropped pronoun (DP) translation problems for neural machine translation models by employing a shared reconstructor and a joint learning approach. |
| Outcome: | The proposed approach improves translation performance and accuracy of DP predictions. |
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe (2020.lrec-1)
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Georg Rehm, Katrin Marheinecke, Stefanie Hegele, Stelios Piperidis, Kalina Bontcheva, Jan Hajič, Khalid Choukri, Andrejs Vasiļjevs, Gerhard Backfried, Christoph Prinz, José Manuel Gómez-Pérez, Luc Meertens, Paul Lukowicz, Josef van Genabith, Andrea Lösch, Philipp Slusallek, Morten Irgens, Patrick Gatellier, Joachim Köhler, Laure Le Bars, Dimitra Anastasiou, Albina Auksoriūtė, Núria Bel, António Branco, Gerhard Budin, Walter Daelemans, Koenraad De Smedt, Radovan Garabík, Maria Gavriilidou, Dagmar Gromann, Svetla Koeva, Simon Krek, Cvetana Krstev, Krister Lindén, Bernardo Magnini, Jan Odijk, Maciej Ogrodniczuk, Eiríkur Rögnvaldsson, Mike Rosner, Bolette Pedersen, Inguna Skadiņa, Marko Tadić, Dan Tufiș, Tamás Váradi, Kadri Vider, Andy Way, François Yvon
| Challenge: | Language Technologies (LTs) are a powerful means to break down language barriers impacting business, cross-lingual and cross-cultural communication in Europe. |
| Approach: | They present an overview of the European LT landscape and the current state of play in industry and the LT market. |
| Outcome: | The present study outlines funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. |
Getting Gender Right in Neural Machine Translation (D18-1)
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| Challenge: | linguistics studies show that the language used by males and females differs in terms of style and syntax. |
| Approach: | They integrate gender information into NMT systems to improve translation quality for multiple language pairs by incorporating gender information to a large dataset. |
| Outcome: | The proposed system significantly improves translation quality for some language pairs. |
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation (2020.acl-main)
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| Challenge: | incorporating backtranslated data from different sources has led to improved results in machine translation (MT) |
| Approach: | They use a low-resource use-case and a high-resourced language pair to test different backtranslation scenarios and employ data selection to optimise the synthetic corpora. |
| Outcome: | The proposed method reduces the amount of data used while maintaining high-quality MT systems. |
Tailoring Neural Architectures for Translating from Morphologically Rich Languages (C18-1)
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| Challenge: | A morphologically complex word is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures. |
| Approach: | They propose a neural architecture which is designed to deal with morphological complexities on the source side and redesign the decoder accordingly to benefit from such information. |
| Outcome: | The proposed model outperforms existing subword- and character-based architectures and showed significant improvements on translating from German, Russian, and Turkish into English. |
gaHealth: An English–Irish Bilingual Corpus of Health Data (2022.lrec-1)
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| Challenge: | Existing models for low-resource languages often focus on creating the largest possible dataset for generic translation. |
| Approach: | They develop a dataset for the specific domain of health for a low-resource English to Irish language pair and compare it to other similar datasets. |
| Outcome: | The proposed model improved BLEU score by 22.2 points compared with top performing models from the LoResMT2021 Shared Task. |
SentAlign: Accurate and Scalable Sentence Alignment (2023.emnlp-demo)
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| Challenge: | SentAlign is an automatic sentence alignment tool designed for large documents . it evaluates all possible alignment paths in documents of thousands of sentences . |
| Approach: | They present a sentence alignment tool that evaluates all possible alignment paths in parallel documents of thousands of sentences and uses a divide-and-conquer approach to align documents containing tens of thousands. |
| Outcome: | The proposed tool outperforms five other sentence alignment tools on two evaluation sets and on a downstream machine translation task. |
SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags (P18-3)
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| Challenge: | Neural Machine Translation models have become the state-of-the-art in the field of machine translation. |
| Approach: | They incorporate semantic supersensetags and syntactic supertag features into EN–FR and EN–DE factored NMT systems and show that they improve model training. |
| Outcome: | The proposed model training improves on EN–FR and EN–DE factored NMT systems. |
Effectively Aligning and Filtering Parallel Corpora under Sparse Data Conditions (2020.acl-srw)
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| Challenge: | Parallel corpora are key to developing good machine translation systems, but abundant parallel data is hard to come by for languages with a low number of speakers. |
| Approach: | They propose an unsupervised alignment method that can handle rich morphology by removing incorrect translations and segments containing extraneous data. |
| Outcome: | The proposed method maximizes the number of correctly translated segments in a corpus and minimises noise by removing incorrect translations and segments containing extraneous data. |
Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering (C18-1)
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| Challenge: | Existing approaches to search result clustering use multiple views and visual and textual views. |
| Approach: | They propose to use multi-view learning to learn search results from web-snippets . they propose to obtain a single consensus partitioning after consulting two views . |
| Outcome: | The proposed approach on a benchmark dataset shows that visual and text-based views can achieve better clustering. |
On Context Span Needed for Machine Translation Evaluation (2020.lrec-1)
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| Challenge: | a number of common patterns can be observed for context-aware MT evaluation, authors say . document-level evaluations have largely been performed at the sentence level . the definition of what constitutes a "document level" evaluation is still unclear . |
| Approach: | They propose to use a series of surveys to identify the necessary context span . they find common patterns that can be used to draw general guidelines . |
| Outcome: | The proposed evaluations of machine translation systems show that some issues and spans depend on domain and target language. |
Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation (N18-1)
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| Challenge: | Morphologically complex words (MCWs) are multi-layer structures consisting of different subunits, each of which carries semantic information and has a specific syntactic role. |
| Approach: | They propose an extension to the state-of-the-art model which works at the character level and boosts the decoder with target-side morphological information. |
| Outcome: | The proposed model improves on the state-of-the-art model and can be extended to include morphologically complex words (MCWs) in three languages. |
Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction (D18-1)
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| Challenge: | Existing approaches to label large-scale data are inadequate for distantly supervised relation extraction (DS-RE). |
| Approach: | They propose a multi-level structured (2-D matrix) self-attention mechanism for DS-RE using bidirectional recurrent neural networks. |
| Outcome: | The proposed framework significantly outperforms baselines on two publicly available DS-RE datasets in terms of PR curves, P@N and F1 measures. |
Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates (N18-1)
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| Challenge: | Temporal orientation refers to an individual’s tendency to connect to the psychological concepts of past, present or future and affects personality, motivation, emotion, decision making and stress coping processes. |
| Approach: | They propose to use a minimally supervised method to classify tweets in one of three temporal categories, past, present, and future, and a deep bi-directional long-term memory (BLSTM) to measure correlation between sentiment view of temporal orientation and different psycho-demographic factors. |
| Outcome: | The proposed method achieves 78.27% accuracy on a manually created test set. |