pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Untranslated Content for Neural Machine Translation
Authors: Isao Goto, Hideki Tanaka
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Machine Translation, NMT, Sentence, Translation

Despite its promise, neural machine translation (NMT) has a serious problem in that source content may be mistakenly left untranslated. The ability to detect untranslated content is important for the practical use of NMT. We evaluate two types of probability with which to detect untranslated content: the cumulative attention (ATN) probability and back translation (BT) probability from the target sentence to the source sentence. Experiments on detecting untranslated content in Japanese-English patent translations show that ATN and BT are each more effective than random choice, BT is more effective than ATN, and the combination of the two provides further improvements. We also confirmed the effectiveness of using ATN and BT to rerank the n-best NMT outputs.
Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation
Authors: Marine Carpuat, Yogarshi Vyas, Xing Niu
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Domain Adaptation, Machine Translation, Natural Language Inference, Sentence, Translation

Parallel corpora are often not as parallel as one might assume: non-literal translations and noisy translations abound, even in curated corpora routinely used for training and evaluation. We use a cross-lingual textual entailment system to distinguish sentence pairs that are parallel in meaning from those that are not, and show that filtering out divergent examples from training improves translation quality.
Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation
Authors: Thiago Castro Ferreira, Iacer Calixto, S Wubben, er, Emiel Krahmer
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: AMR-to-Text Generation, Machine Translation, Text Generation, Translation

In this paper, we study AMR-to-text generation, framing it as a translation task and comparing two different MT approaches (Phrase-based and Neural MT). We systematically study the effects of 3 AMR preprocessing steps (Delexicalisation, Compression, and Linearisation) applied before the MT phase. Our results show that preprocessing indeed helps, although the benefits differ for the two MT models.
A Survey on Intelligent Poetry Generation: Languages, Features, Techniques, Reutilisation and Evaluation
Authors: Hugo Gon{\c{c}}alo Oliveira
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Text Generation

Poetry generation is becoming popular among researchers of Natural Language Generation, Computational Creativity and, broadly, Artificial Intelligence. To produce text that may be regarded as poetry, poetry generation systems are typically knowledge-intensive and have to deal with several levels of language, from lexical to semantics. Interest on the topic resulted in the development of several poetry generators described in the literature, with different features covered or handled differently, by a broad range of alternative approaches, as well as different perspectives on evaluation, another challenging aspect due the underlying subjectivity. This paper surveys intelligent poetry generators around a set of relevant axis for poetry generation {--} targeted languages, form and content features, techniques, reutilisation of material, and evaluation {--} and aims to organise work developed on this topic so far.
Content Selection for Real-time Sports News Construction from Commentary Texts
Authors: Jin-Ge Yao, Jianmin Zhang, Xiaojun Wan, Jianguo Xiao
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Document Summarization, Feature Engineering, Learning-To-Rank, Text Generation

We study the task of constructing sports news report automatically from live commentary and focus on content selection. Rather than receiving every piece of text of a sports match before news construction, as in previous related work, we novelly verify the feasibility of a more challenging but more useful setting to generate news report on the fly by treating live text input as a stream. Specifically, we design various scoring functions to address different requirements of the task. The near submodularity of scoring functions makes it possible to adapt efficient greedy algorithms even in stream data settings. Experiments suggest that our proposed framework can already produce comparable results compared with previous work that relies on a supervised learning-to-rank model with heavy feature engineering.
Improving the Naturalness and Expressivity of Language Generation for Spanish
Authors: Cristina Barros, Dimitra Gkatzia, Elena Lloret
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Text Generation

We present a flexible Natural Language Generation approach for Spanish, focused on the surface realisation stage, which integrates an inflection module in order to improve the naturalness and expressivity of the generated language. This inflection module inflects the verbs using an ensemble of trainable algorithms whereas the other types of words (e.g. nouns, determiners, etc) are inflected using hand-crafted rules. We show that our approach achieves 2{\%} higher accuracy than two state-of-art inflection generation approaches. Furthermore, our proposed approach also predicts an extra feature: the inflection of the imperative mood, which was not taken into account by previous work. We also present a user evaluation, where we demonstrate that the proposed method significantly improves the perceived naturalness of the generated language.
Exploring the Behavior of Classic REG Algorithms in the Description of Characters in 3D Images
Authors: Gonzalo M{\'e}ndez, Raquel Herv{\'a}s, Susana Bautista, Adri{\'a}n Rabad{\'a}n, Teresa Rodr{\'\i}guez
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Image Description, Referring Expression, Referring expression generation, Survey, Text Generation

Describing people and characters can be very useful in different contexts, such as computational narrative or image description for the visually impaired. However, a review of the existing literature shows that the automatic generation of people descriptions has not received much attention. Our work focuses on the description of people in snapshots from a 3D environment. First, we have conducted a survey to identify the way in which people describe other people under different conditions. We have used the information extracted from this survey to design several Referring Expression Generation algorithms which produce similar results. We have evaluated these algorithms with users in order to identify which ones generate the best description for specific characters in different situations. The evaluation has shown that, in order to generate good descriptions, a combination of different algorithms has to be used depending on the features and situation of the person to be described.
Co-PoeTryMe: a Co-Creative Interface for the Composition of Poetry
Authors: Hugo Gon{\c{c}}alo Oliveira, Tiago Mendes, Ana Boavida
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Text Generation

Co-PoeTryMe is a web application for poetry composition, guided by the user, though with the help of automatic features, such as the generation of full (editable) drafts, as well as the acquisition of additional well-formed lines, or semantically-related words, possibly constrained by the number of syllables, rhyme, or polarity. Towards the final poem, the latter can replace lines or words in the draft.
Refer-iTTS: A System for Referring in Spoken Installments to Objects in Real-World Images
Authors: Sina Zarrie{\ss}, M. Soledad L{\'o}pez Gambino, David Schlangen
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Referring Expression, Referring expression generation, Speech Synthesis, Text Generation, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Current referring expression generation systems mostly deliver their output as one-shot, written expressions. We present on-going work on incremental generation of spoken expressions referring to objects in real-world images. This approach extends upon previous work using the words-as-classifier model for generation. We implement this generator in an incremental dialogue processing framework such that we can exploit an existing interface to incremental text-to-speech synthesis. Our system generates and synthesizes referring expressions while continuously observing non-verbal user reactions.
Finding the ``right'' answers for customers
Authors: Frank Schilder
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Text Generation

This talk will present a few NLG systems developed within Thomson Reuters providing information to professionals such as lawyers, accountants or traders. Based on the experience developing these system, I will discuss the usefulness of automatic metrics, crowd-sourced evaluation, corpora studies and expert reviews. I will conclude with exploring the question of whether developers of NLG systems need to follow ethical guidelines and how those guidelines could be established.
Referring Expression Generation under Uncertainty: Algorithm and Evaluation Framework
Authors: Tom Williams, Matthias Scheutz
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Referring Expression, Referring expression generation, Text Generation

For situated agents to effectively engage in natural-language interactions with humans, they must be able to refer to entities such as people, locations, and objects. While classic referring expression generation (REG) algorithms like the Incremental Algorithm (IA) assume perfect, complete, and accessible knowledge of all referents, this is not always possible. In this work, we show how a previously presented consultant framework (which facilitates reference resolution when knowledge is uncertain, heterogeneous and distributed) can be used to extend the IA to produce DIST-PIA, a domain-independent algorithm for REG under uncertain, heterogeneous, and distributed knowledge. We also present a novel framework that can be used to evaluate such REG algorithms without conflating the performance of the algorithm with the performance of classifiers it employs.
Natural Language Descriptions for Human Activities in Video Streams
Authors: Nouf Alharbi, Yoshihiko Gotoh
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Action Recognition, Language Modeling, Language Modelling, Retrieval, Temporal Action Localization, Text Generation, Zero-Shot Action Recognition

There has been continuous growth in the volume and ubiquity of video material. It has become essential to define video semantics in order to aid the searchability and retrieval of this data. We present a framework that produces textual descriptions of video, based on the visual semantic content. Detected action classes rendered as verbs, participant objects converted to noun phrases, visual properties of detected objects rendered as adjectives and spatial relations between objects rendered as prepositions. Further, in cases of zero-shot action recognition, a language model is used to infer a missing verb, aided by the detection of objects and scene settings. These extracted features are converted into textual descriptions using a template-based approach. The proposed video descriptions framework evaluated on the NLDHA dataset using ROUGE scores and human judgment evaluation.
PASS: A Dutch data-to-text system for soccer, targeted towards specific audiences
Authors: Chris van der Lee, Emiel Krahmer, S Wubben, er
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Data-to-Text Generation, Text Generation

We present PASS, a data-to-text system that generates Dutch soccer reports from match statistics. One of the novel elements of PASS is the fact that the system produces corpus-based texts tailored towards fans of one club or the other, which can most prominently be observed in the tone of voice used in the reports. Furthermore, the system is open source and uses a modular design, which makes it relatively easy for people to add extensions. Human-based evaluation shows that people are generally positive towards PASS in regards to its clarity and fluency, and that the tailoring is accurately recognized in most cases.
Evaluation of a Runyankore grammar engine for healthcare messages
Authors: Joan Byamugisha, C. Maria Keet, Brian DeRenzi
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Text Generation

Natural Language Generation (NLG) can be used to generate personalized health information, which is especially useful when provided in one{'}s own language. However, the NLG technique widely used in different domains and languages{---}templates{---}was shown to be inapplicable to Bantu languages, due to their characteristic agglutinative structure. We present here our use of the grammar engine NLG technique to generate text in Runyankore, a Bantu language indigenous to Uganda. Our grammar engine adds to previous work in this field with new rules for cardinality constraints, prepositions in roles, the passive, and phonological conditioning. We evaluated the generated text with linguists and non-linguists, who regarded most text as grammatically correct and understandable; and over 60{\%} of them regarded all the text generated by our system to have been authored by a human being.
Talking about the world with a distributed model
Authors: Gemma Boleda
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: model, Text Generation

We use language to talk about the world, and so reference is a crucial property of language. However, modeling reference is particularly difficult, as it involves both continuous and discrete as-pects of language. For instance, referring expressions like {``}the big mug{''} or {``}it{''} typically contain content words ({``}big{''}, {``}mug{''}), which are notoriously fuzzy or vague in their meaning, and also fun-ction words ({``}the{''}, {``}it{''}) that largely serve as discrete pointers. Data-driven, distributed models based on distributional semantics or deep learning excel at the former, but struggle with the latter, and the reverse is true for symbolic models. I present ongoing work on modeling reference with a distribu-ted model aimed at capturing both aspects, and learns to refer directly from reference acts.
The Code2Text Challenge: Text Generation in Source Libraries
Authors: Kyle Richardson, Sina Zarrie{\ss}, Jonas Kuhn
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Data-to-Text Generation, Text Generation

We propose a new shared task for tactical data-to-text generation in the domain of source code libraries. Specifically, we focus on text generation of function descriptions from example software projects. Data is drawn from existing resources used for studying the related problem of semantic parser induction, and spans a wide variety of both natural languages and programming languages. In this paper, we describe these existing resources, which will serve as training and development data for the task, and discuss plans for building new independent test sets.
Shared Task Proposal: Multilingual Surface Realization Using Universal Dependency Trees
Authors: Simon Mille, Bernd Bohnet, Leo Wanner, Anja Belz
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, POS, Text 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.
The WebNLG Challenge: Generating Text from RDF Data
Authors: Claire Gardent, Anastasia Shimorina, Shashi Narayan, Laura Perez-Beltrachini
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Referring Expression, Referring expression generation, Sentence, Sentence segmentation, Text Generation

The WebNLG challenge consists in mapping sets of RDF triples to text. It provides a common benchmark on which to train, evaluate and compare {``}microplanners{''}, i.e. generation systems that verbalise a given content by making a range of complex interacting choices including referring expression generation, aggregation, lexicalisation, surface realisation and sentence segmentation. In this paper, we introduce the microplanning task, describe data preparation, introduce our evaluation methodology, analyse participant results and provide a brief description of the participating systems.
A Commercial Perspective on Reference
Authors: Ehud Reiter
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Referring Expression, Text Generation

I briefly describe some of the commercial work which XXX is doing in referring expression algorithms, and highlight differences between what is commercially important (at least to XXX) and the NLG research literature. In particular, XXX is less interested in generic reference algorithms than in high-quality algorithms for specific types of references, such as components of machines, named entities, and dates.
Integrated sentence generation using charts
Authors: Alex Koller, er, Nikos Engonopoulos
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Sentence, Text Generation

Integrating surface realization and the generation of referring expressions into a single algorithm can improve the quality of the generated sentences. Existing algorithms for doing this, such as SPUD and CRISP, are search-based and can be slow or incomplete. We offer a chart-based algorithm for integrated sentence generation and demonstrate its runtime efficiency.
Adapting SimpleNLG to Spanish
Authors: Alej Ramos-Soto, ro, Julio Janeiro-Gallardo, Alberto Bugar{\'\i}n Diz
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Text Generation

We describe SimpleNLG-ES, an adaptation of the SimpleNLG realization library for the Spanish language. Our implementation is based on the bilingual English-French SimpleNLG-EnFr adaptation. The library has been tested using a battery of examples that ensure that the most common syntax, morphology and orthography rules for Spanish are met. The library is currently being used in three different projects for the development of data-to-text systems in the meteorological, statistical data information, and business intelligence application domains.
G-TUNA: a corpus of referring expressions in German, including duration information
Authors: David Howcroft, Jorrig Vogels, Vera Demberg
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Referring Expression, Referring expression generation, Text Generation

Corpora of referring expressions elicited from human participants in a controlled environment are an important resource for research on automatic referring expression generation. We here present G-TUNA, a new corpus of referring expressions for German. Using the furniture stimuli set developed for the TUNA and D-TUNA corpora, our corpus extends on these corpora by providing data collected in a simulated driving dual-task setting, and additionally provides exact duration annotations for the spoken referring expressions. This corpus will hence allow researchers to analyze the interaction between referring expression length and speech rate, under conditions where the listener is under high vs. low cognitive load.
Toward an NLG System for Bantu languages: first steps with Runyankore (demo)
Authors: Joan Byamugisha, C. Maria Keet, Brian DeRenzi
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Text Generation

There are many domain-specific and language-specific NLG systems, of which it may be possible to adapt to related domains and languages. The languages in the Bantu language family have their own set of features distinct from other major groups, which therefore severely limits the options to bootstrap an NLG system from existing ones. We present here our first proof-of-concept application for knowledge-to-text NLG as a plugin to the Protege 5.x ontology development system, tailored to Runyankore, a Bantu language indigenous to Uganda. It comprises a basic annotation model for linguistic information such as noun class, an implementation of existing verbalisation rules and a CFG for verbs, and a basic interface for data entry.
A working, non-trivial, topically indifferent NLG System for 17 languages
Authors: Robert Wei{\ss}graeber, Andreas Madsack
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Text Generation

A fully fledged practical working application for a rule-based NLG system is presented that is able to create non-trivial, human sounding narrative from structured data, in any language and for any topic.
Generating titles for millions of browse pages on an e-Commerce site
Authors: Prashant Mathur, Nicola Ueffing, Gregor Leusch
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Automatic Post-Editing, Machine Translation, Question Answering, Text Generation, Translation

We present two approaches to generate titles for browse pages in five different languages, namely English, German, French, Italian and Spanish. These browse pages are structured search pages in an e-commerce domain. We first present a rule-based approach to generate these browse page titles. In addition, we also present a hybrid approach which uses a phrase-based statistical machine translation engine on top of the rule-based system to assemble the best title. For the two languages English and German we have access to a large amount of already available rule-based generated and curated titles. For these languages we present an automatic post-editing approach which learns how to post-edit the rule-based titles into curated titles.
Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores
Authors: Hongyu Zang, Xiaojun Wan
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Data-to-Text Generation, Decoder, Retrieval, Text Generation

Data-to-text generation is very essential and important in machine writing applications. The recent deep learning models, like Recurrent Neural Networks (RNNs), have shown a bright future for relevant text generation tasks. However, rare work has been done for automatic generation of long reviews from user opinions. In this paper, we introduce a deep neural network model to generate long Chinese reviews from aspect-sentiment scores representing users{'} opinions. We conduct our study within the framework of encoder-decoder networks, and we propose a hierarchical structure with aligned attention in the Long-Short Term Memory (LSTM) decoder. Experiments show that our model outperforms retrieval based baseline methods, and also beats the sequential generation models in qualitative evaluations.
A model of suspense for narrative generation
Authors: Richard Doust, Paul Piwek
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: model, Sentence, Text Generation

Most work on automatic generation of narratives, and more specifically suspenseful narrative, has focused on detailed domain-specific modelling of character psychology and plot structure. Recent work in computational linguistics on the automatic learning of narrative schemas suggests an alternative approach that exploits such schemas as a starting point for modelling and measuring suspense. We propose a domain-independent model for tracking suspense in a story which can be used to predict the audience{'}s suspense response on a sentence-by-sentence basis at the content determination stage of narrative generation. The model lends itself as the theoretical foundation for a suspense module that is compatible with alternative narrative generation theories. The proposal is evaluated by human judges{'} normalised average scores correlate strongly with predicted values.