Neural Text Generation from Structured Data with Application to the
            Biography Domain

Lebret, Rémi; Grangier, David; Auli, Michael

doi:10.18653/v1/d16-1128

Cited by 400 publications

(609 citation statements)

References 39 publications

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“…The first shared task at the workshop focused on document-level generation and translation. Many recent attempts at NLG have focused on sentencelevel generation (Lebret et al, 2016;Gardent et al, 2017). However, real world language generation applications tend to involve generation of much larger amount of text such as dialogues or multisentence summaries.…”

Section: Shared Task: Document-levelmentioning

confidence: 99%

Findings of the Third Workshop on Neural Generation and Translation

Hayashi¹,

Oda²,

Birch³

et al. 2019

Proceedings of the 3rd Workshop on Neural Generation and Translation

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This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.

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Section: Shared Task: Document-levelmentioning

confidence: 99%

Findings of the Third Workshop on Neural Generation and Translation

Hayashi¹,

Oda²,

Birch³

et al. 2019

Proceedings of the 3rd Workshop on Neural Generation and Translation

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“…Targeting this newly emerging demand, some models have been proposed to respond by generating natural language replies on the y, rather than by (re)ranking a xed set of items or extracting passages from existing pages. Examples are conversational and dialog systems [7,34,54] or machine reading and question answering tasks where the model either infers the answer from unstructured data, like textual documents that do not necessarily feature the answer literally [21,22,46,56], or generates natural language given structured data, like data from knowledge graphs or from external memories [1,18,33,37,40].…”

Section: Objectivesmentioning

confidence: 99%

Neural Networks for Information Retrieval

Kenter¹,

Borisov

Gysel

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modernday research has given rise to many di erent approaches for many di erent IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they bene t IR research. It covers key architectures, as well as the most promising future directions. MOTIVATIONPrompted by the advances of deep learning in computer vision research, neural networks have resurfaced as a popular machine learning paradigm in many other directions of research as well, including information retrieval. Recent years have seen neural networks being applied to all key parts of the typical modern IR pipeline, such core ranking algorithms [26,42,51], click models [9,10], knowledge graphs [8,35], text similarity [28,47], entity retrieval [52,53], language modeling [5], question answering [22,56], and dialogue systems [34,54].A key advantage that sets neural networks apart from many learning strategies employed earlier, is their ability to work from raw input data. E.g., when given enough training data, well-designed networks can become feature extractors themselves, e.g., incorporating basic input characteristics such as term frequency (tf) and term saliency (idf)-that used to be pre-calculated o ine-in their initial layers. Where designing features used to be a crucial aspect and contribution of newly proposed IR approaches, the focus * Corresponding author.SIGIR '17, Shinjuku, Tokyo, Japan has shifted to designing network architectures instead. As a consequence, many di erent architectures and paradigms have been proposed, such as auto-encoders, recursive networks, recurrent networks, convolutional networks, various embedding methods, deep reinforcement and deep q-learning, and, more recently, generative adversarial networks, of which most have been applied in IR settings. The aim of the neural networks for IR (NN4IR) tutorial is to provide a clear overview of the main network architectures currently applied in IR and to show explicitly how they relate to previous work. The tutorial covers methods applied in industry and academia, with in-depth insights into the underlying theory, core IR tasks, applicability, key assets and handicaps, scalability concerns and practical tips and tricks.We expect the tutorial to be useful both for academic and industrial researchers and practitioners who either want to develop new neural models, use them in their own research in other areas or apply the models described here to improve actual IR systems. OBJECTIVESThe material in the tutorial covers a broad range of IR applications. It is structured as follows:Preliminaries (60 minutes). The rece...

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“…Lebret et al (2016) introduced a neural model for this concept-to-text generation and evaluated on a large dataset of biographies from Wikipedia. Chisholm et.…”

Section: Previous Workmentioning

confidence: 99%

Generating titles for millions of browse pages on an e-Commerce site

Mathur

Ueffing

Leusch

2017

Proceedings of the 10th International Conference on Natural Language Generation

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We present three 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 rule-based generated and human-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.

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Neural Text Generation from Structured Data with Application to the Biography Domain

Cited by 400 publications

References 39 publications

Findings of the Third Workshop on Neural Generation and Translation

Findings of the Third Workshop on Neural Generation and Translation

Neural Networks for Information Retrieval

Generating titles for millions of browse pages on an e-Commerce site

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