Simon Mille


2021

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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann | Tosin Adewumi | Karmanya Aggarwal | Pawan Sasanka Ammanamanchi | Anuoluwapo Aremu | Antoine Bosselut | Khyathi Raghavi Chandu | Miruna-Adriana Clinciu | Dipanjan Das | Kaustubh Dhole | Wanyu Du | Esin Durmus | Ondřej Dušek | Chris Chinenye Emezue | Varun Gangal | Cristina Garbacea | Tatsunori Hashimoto | Yufang Hou | Yacine Jernite | Harsh Jhamtani | Yangfeng Ji | Shailza Jolly | Mihir Kale | Dhruv Kumar | Faisal Ladhak | Aman Madaan | Mounica Maddela | Khyati Mahajan | Saad Mahamood | Bodhisattwa Prasad Majumder | Pedro Henrique Martins | Angelina McMillan-Major | Simon Mille | Emiel van Miltenburg | Moin Nadeem | Shashi Narayan | Vitaly Nikolaev | Andre Niyongabo Rubungo | Salomey Osei | Ankur Parikh | Laura Perez-Beltrachini | Niranjan Ramesh Rao | Vikas Raunak | Juan Diego Rodriguez | Sashank Santhanam | João Sedoc | Thibault Sellam | Samira Shaikh | Anastasia Shimorina | Marco Antonio Sobrevilla Cabezudo | Hendrik Strobelt | Nishant Subramani | Wei Xu | Diyi Yang | Akhila Yerukola | Jiawei Zhou
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.

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Text-in-Context: Token-Level Error Detection for Table-to-Text Generation
Zdeněk Kasner | Simon Mille | Ondřej Dušek
Proceedings of the 14th International Conference on Natural Language Generation

We present our Charles-UPF submission for the Shared Task on Evaluating Accuracy in Generated Texts at INLG 2021. Our system can detect the errors automatically using a combination of a rule-based natural language generation (NLG) system and pretrained language models (LMs). We first utilize a rule-based NLG system to generate sentences with facts that can be derived from the input. For each sentence we evaluate, we select a subset of facts which are relevant by measuring semantic similarity to the sentence in question. Finally, we finetune a pretrained language model on annotated data along with the relevant facts for fine-grained error detection. On the test set, we achieve 69% recall and 75% precision with a model trained on a mixture of human-annotated and synthetic data.

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Another PASS: A Reproduction Study of the Human Evaluation of a Football Report Generation System
Simon Mille | Thiago Castro Ferreira | Anya Belz | Brian Davis
Proceedings of the 14th International Conference on Natural Language Generation

This paper reports results from a reproduction study in which we repeated the human evaluation of the PASS Dutch-language football report generation system (van der Lee et al., 2017). The work was carried out as part of the ReproGen Shared Task on Reproducibility of Human Evaluations in NLG, in Track A (Paper 1). We aimed to repeat the original study exactly, with the main difference that a different set of evaluators was used. We describe the study design, present the results from the original and the reproduction study, and then compare and analyse the differences between the two sets of results. For the two ‘headline’ results of average Fluency and Clarity, we find that in both studies, the system was rated more highly for Clarity than for Fluency, and Clarity had higher standard deviation. Clarity and Fluency ratings were higher, and their standard deviations lower, in the reproduction study than in the original study by substantial margins. Clarity had a higher degree of reproducibility than Fluency, as measured by the coefficient of variation. Data and code are publicly available.

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Assessing the Syntactic Capabilities of Transformer-based Multilingual Language Models
Laura Pérez-Mayos | Alba Táboas García | Simon Mille | Leo Wanner
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions
David M. Howcroft | Anya Belz | Miruna-Adriana Clinciu | Dimitra Gkatzia | Sadid A. Hasan | Saad Mahamood | Simon Mille | Emiel van Miltenburg | Sashank Santhanam | Verena Rieser
Proceedings of the 13th International Conference on Natural Language Generation

Human assessment remains the most trusted form of evaluation in NLG, but highly diverse approaches and a proliferation of different quality criteria used by researchers make it difficult to compare results and draw conclusions across papers, with adverse implications for meta-evaluation and reproducibility. In this paper, we present (i) our dataset of 165 NLG papers with human evaluations, (ii) the annotation scheme we developed to label the papers for different aspects of evaluations, (iii) quantitative analyses of the annotations, and (iv) a set of recommendations for improving standards in evaluation reporting. We use the annotations as a basis for examining information included in evaluation reports, and levels of consistency in approaches, experimental design and terminology, focusing in particular on the 200+ different terms that have been used for evaluated aspects of quality. We conclude that due to a pervasive lack of clarity in reports and extreme diversity in approaches, human evaluation in NLG presents as extremely confused in 2020, and that the field is in urgent need of standard methods and terminology.

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Disentangling the Properties of Human Evaluation Methods: A Classification System to Support Comparability, Meta-Evaluation and Reproducibility Testing
Anya Belz | Simon Mille | David M. Howcroft
Proceedings of the 13th International Conference on Natural Language Generation

Current standards for designing and reporting human evaluations in NLP mean it is generally unclear which evaluations are comparable and can be expected to yield similar results when applied to the same system outputs. This has serious implications for reproducibility testing and meta-evaluation, in particular given that human evaluation is considered the gold standard against which the trustworthiness of automatic metrics is gauged. %and merging others, as well as deciding which evaluations should be able to reproduce each other’s results. Using examples from NLG, we propose a classification system for evaluations based on disentangling (i) what is being evaluated (which aspect of quality), and (ii) how it is evaluated in specific (a) evaluation modes and (b) experimental designs. We show that this approach provides a basis for determining comparability, hence for comparison of evaluations across papers, meta-evaluation experiments, reproducibility testing.

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Proceedings of the Third Workshop on Multilingual Surface Realisation
Anya Belz | Bernd Bohnet | Thiago Castro Ferreira | Yvette Graham | Simon Mille | Leo Wanner
Proceedings of the Third Workshop on Multilingual Surface Realisation

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The Third Multilingual Surface Realisation Shared Task (SR’20): Overview and Evaluation Results
Simon Mille | Anya Belz | Bernd Bohnet | Thiago Castro Ferreira | Yvette Graham | Leo Wanner
Proceedings of the Third Workshop on Multilingual Surface Realisation

This paper presents results from the Third Shared Task on Multilingual Surface Realisation (SR’20) which was organised as part of the COLING’20 Workshop on Multilingual Surface Realisation. As in SR’18 and SR’19, the shared task comprised two tracks: (1) a Shallow Track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (2) a Deep Track where additionally, functional words and morphological information were removed. Moreover, each track had two subtracks: (a) restricted-resource, where only the data provided or approved as part of a track could be used for training models, and (b) open-resource, where any data could be used. The Shallow Track was offered in 11 languages, whereas the Deep Track in 3 ones. Systems were evaluated using both automatic metrics and direct assessment by human evaluators in terms of Readability and Meaning Similarity to reference outputs. We present the evaluation results, along with descriptions of the SR’19 tracks, data and evaluation methods, as well as brief summaries of the participating systems. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.

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Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
Thiago Castro Ferreira | Claire Gardent | Nikolai Ilinykh | Chris van der Lee | Simon Mille | Diego Moussallem | Anastasia Shimorina
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

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A Case Study of NLG from Multimedia Data Sources: Generating Architectural Landmark Descriptions
Simon Mille | Spyridon Symeonidis | Maria Rousi | Montserrat Marimon Felipe | Klearchos Stavrothanasopoulos | Petros Alvanitopoulos | Roberto Carlini Salguero | Jens Grivolla | Georgios Meditskos | Stefanos Vrochidis | Leo Wanner
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

In this paper, we present a pipeline system that generates architectural landmark descriptions using textual, visual and structured data. The pipeline comprises five main components:(i) a textual analysis component, which extracts information from Wikipedia pages; (ii)a visual analysis component, which extracts information from copyright-free images; (iii) a retrieval component, which gathers relevant (property, subject, object) triples from DBpedia; (iv) a fusion component, which stores the contents from the different modalities in a Knowledge Base (KB) and resolves the conflicts that stem from using different sources of information; (v) an NLG component, which verbalises the resulting contents of the KB. We show that thanks to the addition of other modalities, we can make the verbalisation of DBpedia triples more relevant and/or inspirational.

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A General Benchmarking Framework for Text Generation
Diego Moussallem | Paramjot Kaur | Thiago Ferreira | Chris van der Lee | Anastasia Shimorina | Felix Conrads | Michael Röder | René Speck | Claire Gardent | Simon Mille | Nikolai Ilinykh | Axel-Cyrille Ngonga Ngomo
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

The RDF-to-text task has recently gained substantial attention due to the continuous growth of RDF knowledge graphs in number and size. Recent studies have focused on systematically comparing RDF-to-text approaches on benchmarking datasets such as WebNLG. Although some evaluation tools have already been proposed for text generation, none of the existing solutions abides by the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles and involves RDF data for the knowledge extraction task. In this paper, we present BENG, a FAIR benchmarking platform for Natural Language Generation (NLG) and Knowledge Extraction systems with focus on RDF data. BENG builds upon the successful benchmarking platform GERBIL, is opensource and is publicly available along with the data it contains.

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The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020)
Thiago Castro Ferreira | Claire Gardent | Nikolai Ilinykh | Chris van der Lee | Simon Mille | Diego Moussallem | Anastasia Shimorina
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

WebNLG+ offers two challenges: (i) mapping sets of RDF triples to English or Russian text (generation) and (ii) converting English or Russian text to sets of RDF triples (semantic parsing). Compared to the eponymous WebNLG challenge, WebNLG+ provides an extended dataset that enable the training, evaluation, and comparison of microplanners and semantic parsers. In this paper, we present the results of the generation and semantic parsing task for both English and Russian and provide a brief description of the participating systems.

2019

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Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)
Simon Mille | Anja Belz | Bernd Bohnet | Yvette Graham | Leo Wanner
Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)

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The Second Multilingual Surface Realisation Shared Task (SR’19): Overview and Evaluation Results
Simon Mille | Anja Belz | Bernd Bohnet | Yvette Graham | Leo Wanner
Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)

We report results from the SR’19 Shared Task, the second edition of a multilingual surface realisation task organised as part of the EMNLP’19 Workshop on Multilingual Surface Realisation. As in SR’18, the shared task comprised two tracks with different levels of complexity: (a) a shallow track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (b) a deep track where additionally, functional words and morphological information were removed. The shallow track was offered in eleven, and the deep track in three languages. Systems were evaluated (a) automatically, using a range of intrinsic metrics, and (b) by human judges in terms of readability and meaning similarity. This report presents the evaluation results, along with descriptions of the SR’19 tracks, data and evaluation methods. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.

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Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations
Marco Antonio Sobrevilla Cabezudo | Simon Mille | Thiago Pardo
Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)

This paper presents an exploratory study that aims to evaluate the usefulness of back-translation in Natural Language Generation (NLG) from semantic representations for non-English languages. Specifically, Abstract Meaning Representation and Brazilian Portuguese (BP) are chosen as semantic representation and language, respectively. Two methods (focused on Statistical and Neural Machine Translation) are evaluated on two datasets (one automatically generated and another one human-generated) to compare the performance in a real context. Also, several cuts according to quality measures are performed to evaluate the importance (or not) of the data quality in NLG. Results show that there are still many improvements to be made but this is a promising approach.

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Teaching FORGe to Verbalize DBpedia Properties in Spanish
Simon Mille | Stamatia Dasiopoulou | Beatriz Fisas | Leo Wanner
Proceedings of the 12th International Conference on Natural Language Generation

Statistical generators increasingly dominate the research in NLG. However, grammar-based generators that are grounded in a solid linguistic framework remain very competitive, especially for generation from deep knowledge structures. Furthermore, if built modularly, they can be ported to other genres and languages with a limited amount of work, without the need of the annotation of a considerable amount of training data. One of these generators is FORGe, which is based on the Meaning-Text Model. In the recent WebNLG challenge (the first comprehensive task addressing the mapping of RDF triples to text) FORGe ranked first with respect to the overall quality in human evaluation. We extend the coverage of FORGE’s open source grammatical and lexical resources for English, so as to further improve the English texts, and port them to Spanish, to achieve a comparable quality. This confirms that, as already observed in the case of SimpleNLG, a robust universal grammar-driven framework and a systematic organization of the linguistic resources can be an adequate choice for NLG applications.

2018

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Proceedings of the First Workshop on Multilingual Surface Realisation
Simon Mille | Anja Belz | Bernd Bohnet | Emily Pitler | Leo Wanner
Proceedings of the First Workshop on Multilingual Surface Realisation

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The First Multilingual Surface Realisation Shared Task (SR’18): Overview and Evaluation Results
Simon Mille | Anja Belz | Bernd Bohnet | Yvette Graham | Emily Pitler | Leo Wanner
Proceedings of the First Workshop on Multilingual Surface Realisation

We report results from the SR’18 Shared Task, a new multilingual surface realisation task organised as part of the ACL’18 Workshop on Multilingual Surface Realisation. As in its English-only predecessor task SR’11, the shared task comprised two tracks with different levels of complexity: (a) a shallow track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (b) a deep track where additionally, functional words and morphological information were removed. The shallow track was offered in ten, and the deep track in three languages. Systems were evaluated (a) automatically, using a range of intrinsic metrics, and (b) by human judges in terms of readability and meaning similarity. This report presents the evaluation results, along with descriptions of the SR’18 tracks, data and evaluation methods. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.

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Underspecified Universal Dependency Structures as Inputs for Multilingual Surface Realisation
Simon Mille | Anja Belz | Bernd Bohnet | Leo Wanner
Proceedings of the 11th International Conference on Natural Language Generation

In this paper, we present the datasets used in the Shallow and Deep Tracks of the First Multilingual Surface Realisation Shared Task (SR’18). For the Shallow Track, data in ten languages has been released: Arabic, Czech, Dutch, English, Finnish, French, Italian, Portuguese, Russian and Spanish. For the Deep Track, data in three languages is made available: English, French and Spanish. We describe in detail how the datasets were derived from the Universal Dependencies V2.0, and report on an evaluation of the Deep Track input quality. In addition, we examine the motivation for, and likely usefulness of, deriving NLG inputs from annotations in resources originally developed for Natural Language Understanding (NLU), and assess whether the resulting inputs supply enough information of the right kind for the final stage in the NLG process.

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Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem
Alexander Shvets | Simon Mille | Leo Wanner
Proceedings of the 11th International Conference on Natural Language Generation

An increasing amount of research tackles the challenge of text generation from abstract ontological or semantic structures, which are in their very nature potentially large connected graphs. These graphs must be “packaged” into sentence-wise subgraphs. We interpret the problem of sentence packaging as a community detection problem with post optimization. Experiments on the texts of the VerbNet/FrameNet structure annotated-Penn Treebank, which have been converted into graphs by a coreference merge using Stanford CoreNLP, show a high F1-score of 0.738.

2017

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Shared Task Proposal: Multilingual Surface Realization Using Universal Dependency Trees
Simon Mille | Bernd Bohnet | Leo Wanner | Anja Belz
Proceedings of the 10th International Conference on Natural Language Generation

We propose a shared task on multilingual Surface Realization, i.e., on mapping unordered and uninflected universal dependency trees to correctly ordered and inflected sentences in a number of languages. A second deeper input will be available in which, in addition, functional words, fine-grained PoS and morphological information will be removed from the input trees. The first shared task on Surface Realization was carried out in 2011 with a similar setup, with a focus on English. We think that it is time for relaunching such a shared task effort in view of the arrival of Universal Dependencies annotated treebanks for a large number of languages on the one hand, and the increasing dominance of Deep Learning, which proved to be a game changer for NLP, on the other hand.

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A demo of FORGe: the Pompeu Fabra Open Rule-based Generator
Simon Mille | Leo Wanner
Proceedings of the 10th International Conference on Natural Language Generation

This demo paper presents the multilingual deep sentence generator developed by the TALN group at Universitat Pompeu Fabra, implemented as a series of rule-based graph-transducers for the syntacticization of the input graphs, the resolution of morphological agreements, and the linearization of the trees.

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FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers
Simon Mille | Roberto Carlini | Alicia Burga | Leo Wanner
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We present the contribution of Universitat Pompeu Fabra’s NLP group to the SemEval Task 9.2 (AMR-to-English Generation). The proposed generation pipeline comprises: (i) a series of rule-based graph-transducers for the syntacticization of the input graphs and the resolution of morphological agreements, and (ii) an off-the-shelf statistical linearization component.

2015

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Towards a multi-layered dependency annotation of Finnish
Alicia Burga | Simon Mille | Anton Granvik | Leo Wanner
Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)

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Data-driven sentence generation with non-isomorphic trees
Miguel Ballesteros | Bernd Bohnet | Simon Mille | Leo Wanner
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Visualizing Deep-Syntactic Parser Output
Juan Soler-Company | Miguel Ballesteros | Bernd Bohnet | Simon Mille | Leo Wanner
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

2014

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Classifiers for data-driven deep sentence generation
Miguel Ballesteros | Simon Mille | Leo Wanner
Proceedings of the 8th International Natural Language Generation Conference (INLG)

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Deep-Syntactic Parsing
Miguel Ballesteros | Bernd Bohnet | Simon Mille | Leo Wanner
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Exploring Morphosyntactic Annotation over a Spanish Corpus for Dependency Parsing
Miguel Ballesteros | Simon Mille | Alicia Burga
Proceedings of the Second International Conference on Dependency Linguistics (DepLing 2013)

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AnCora-UPF: A Multi-Level Annotation of Spanish
Simon Mille | Alicia Burga | Leo Wanner
Proceedings of the Second International Conference on Dependency Linguistics (DepLing 2013)

2012

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Towards a Surface Realization-Oriented Corpus Annotation
Leo Wanner | Simon Mille | Bernd Bohnet
INLG 2012 Proceedings of the Seventh International Natural Language Generation Conference

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The Surface Realisation Task: Recent Developments and Future Plans
Anja Belz | Bernd Bohnet | Simon Mille | Leo Wanner | Michael White
INLG 2012 Proceedings of the Seventh International Natural Language Generation Conference

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Text Simplification Tools for Spanish
Stefan Bott | Horacio Saggion | Simon Mille
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In this paper we describe the development of a text simplification system for Spanish. Text simplification is the adaptation of a text to the special needs of certain groups of readers, such as language learners, people with cognitive difficulties and elderly people, among others. There is a clear need for simplified texts, but manual production and adaptation of existing texts is labour intensive and costly. Automatic simplification is a field which attracts growing attention in Natural Language Processing, but, to the best of our knowledge, there are no simplification tools for Spanish. We present a prototype for automatic simplification, which shows that the most important structural simplification operations can be successfully treated with an approach based on rules which can potentially be improved by statistical methods. For the development of this prototype we carried out a corpus study which aims at identifying the operations a text simplification system needs to carry out in order to produce an output similar to what human editors produce when they simplify texts.

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How Does the Granularity of an Annotation Scheme Influence Dependency Parsing Performance?
Simon Mille | Alicia Burga | Gabriela Ferraro | Leo Wanner
Proceedings of COLING 2012: Posters

2011

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<StuMaBa>: From Deep Representation to Surface
Bernd Bohnet | Simon Mille | Benoît Favre | Leo Wanner
Proceedings of the 13th European Workshop on Natural Language Generation

2010

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Syntactic Dependencies for Multilingual and Multilevel Corpus Annotation
Simon Mille | Leo Wanner
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The relevance of syntactic dependency annotated corpora is nowadays unquestioned. However, a broad debate on the optimal set of dependency relation tags did not take place yet. As a result, largely varying tag sets of a largely varying size are used in different annotation initiatives. We propose a hierarchical dependency structure annotation schema that is more detailed and more flexible than the known annotation schemata. The schema allows us to choose the level of the desired detail of annotation, which facilitates the use of the schema for corpus annotation for different languages and for different NLP applications. Thanks to the inclusion of semantico-syntactic tags into the schema, we can annotate a corpus not only with syntactic dependency structures, but also with valency patterns as they are usually found in separate treebanks such as PropBank and NomBank. Semantico-syntactic tags and the level of detail of the schema furthermore facilitate the derivation of deep-syntactic and semantic annotations, leading to truly multilevel annotated dependency corpora. Such multilevel annotations can be readily used for the task of ML-based acquisition of grammar resources that map between the different levels of linguistic representation ― something which forms part of, for instance, any natural language text generator.

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Broad Coverage Multilingual Deep Sentence Generation with a Stochastic Multi-Level Realizer
Bernd Bohnet | Leo Wanner | Simon Mille | Alicia Burga
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2008

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Making Text Resources Accessible to the Reader: the Case of Patent Claims
Simon Mille | Leo Wanner
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Hardly any other kind of text structures is as notoriously difficult to read as patents. This is first of all due to their abstract vocabulary and their very complex syntactic constructions. Especially the claims in a patent are a challenge: in accordance with international patent writing regulations, each claim must be rendered in a single sentence. As a result, sentences with more than 200 words are not uncommon. Therefore, paraphrasing of the claims in terms the user can understand is of high demand. We present a rule-based paraphrasing module that realizes paraphrasing of patent claims in English as a rewriting task. Prior to the rewriting proper, the module implies the stages of simplification and discourse and syntactic analyses. The rewriting makes use of a full-fledged text generator and consists in a number of genuine generation tasks such as aggregation, selection of referring expressions, choice of discourse markers and syntactic generation. As generator, we use the MATE-work bench, which is based on the Meaning-Text Theory of linguistics.

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Multilingual summarization in practice: the case of patent claims
Simon Mille | Leo Wanner
Proceedings of the 12th Annual conference of the European Association for Machine Translation

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