ymoslem/acl-abstracts huggingface dataset:
Automatically Generating Questions about Novel Metaphors in Literature (2018)

The automatic generation of stimulating questions is crucial to the development of intelligent cognitive exercise applications. We developed an approach that generates appropriate \textit{Questioning the Author} queries based on novel metaphors in diverse syntactic relations in literature. We show that the generated questions are comparable to human-generated questions in terms of naturalness, sensibility, and depth, and score slightly higher than human-generated questions in terms of clarity. We also show that questions generated about novel metaphors are rated as cognitively deeper than questions generated about non- or conventional metaphors, providing evidence that metaphor novelty can be leveraged to promote cognitive exercise.
A Master-Apprentice Approach to Automatic Creation of Culturally Satirical Movie Titles (2018)

Satire has played a role in indirectly expressing critique towards an authority or a person from time immemorial. We present an autonomously creative master-apprentice approach consisting of a genetic algorithm and an NMT model to produce humorous and culturally apt satire out of movie titles automatically. Furthermore, we evaluate the approach in terms of its creativity and its output. We provide a solid definition for creativity to maximize the objectiveness of the evaluation.
Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring? (2018)

Responses in task-oriented dialogue systems often realize multiple propositions whose ultimate form depends on the use of sentence planning and discourse structuring operations. For example a recommendation may consist of an explicitly evaluative utterance e.g. \textit{Chanpen Thai is the best option}, along with content related by the justification discourse relation, e.g. \textit{It has great food and service}, that combines multiple propositions into a single phrase. While neural generation methods integrate sentence planning and surface realization in one end-to-end learning framework, previous work has not shown that neural generators can: (1) perform common sentence planning and discourse structuring operations; (2) make decisions as to whether to realize content in a single sentence or over multiple sentences; (3) generalize sentence planning and discourse relation operations beyond what was seen in training. We systematically create large training corpora that exhibit particular sentence planning operations and then test neural models to see what they learn. We compare models without explicit latent variables for sentence planning with ones that provide explicit supervision during training. We show that only the models with additional supervision can reproduce sentence planning and discourse operations and generalize to situations unseen in training.
Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features (2018)

Question Generation is the task of automatically creating questions from textual input. In this work we present a new Attentional Encoder{--}Decoder Recurrent Neural Network model for automatic question generation. Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels. The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution. In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence. Our model achieves state of the art results of 19.98 Bleu{\_}4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin. A human evaluation also shows that the added features improve the quality of the generated questions.
Evaluation methodologies in Automatic Question Generation 2013-2018 (2018)

In the last few years Automatic Question Generation (AQG) has attracted increasing interest. In this paper we survey the evaluation methodologies used in AQG. Based on a sample of 37 papers, our research shows that the systems' development has not been accompanied by similar developments in the methodologies used for the systems' evaluation. Indeed, in the papers we examine here, we find a wide variety of both intrinsic and extrinsic evaluation methodologies. Such diverse evaluation practices make it difficult to reliably compare the quality of different generation systems. Our study suggests that, given the rapidly increasing level of research in the area, a common framework is urgently needed to compare the performance of AQG systems and NLG systems more generally.
Task Proposal: The TL;DR Challenge (2018)

The TL;DR challenge fosters research in abstractive summarization of informal text, the largest and fastest-growing source of textual data on the web, which has been overlooked by summarization research so far. The challenge owes its name to the frequent practice of social media users to supplement long posts with a {\textquotedblleft}TL;DR{\textquotedblright}{---}for {\textquotedblleft}too long; didn`t read{\textquotedblright}{---}followed by a short summary as a courtesy to those who would otherwise reply with the exact same abbreviation to indicate they did not care to read a post for its apparent length. Posts featuring TL;DR summaries form an excellent ground truth for summarization, and by tapping into this resource for the first time, we have mined millions of training examples from social media, opening the door to all kinds of generative models.
Findings of the E2E NLG Challenge (2018)

This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems. Recent end-to-end generation systems are promising since they reduce the need for data annotation. However, they are currently limited to small, delexicalised datasets. The E2E NLG shared task aims to assess whether these novel approaches can generate better-quality output by learning from a dataset containing higher lexical richness, syntactic complexity and diverse discourse phenomena. We compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures {--} with the majority implementing sequence-to-sequence models (seq2seq) {--} as well as systems based on grammatical rules and templates.
Adapting Descriptions of People to the Point of View of a Moving Observer (2018)

This paper addresses the task of generating descriptions of people for an observer that is moving within a scene. As the observer moves, the descriptions of the people around him also change. A referring expression generation algorithm adapted to this task needs to continuously monitor the changes in the field of view of the observer, his relative position to the people being described, and the relative position of these people to any landmarks around them, and to take these changes into account in the referring expressions generated. This task presents two advantages: many of the mechanisms already available for static contexts may be applied with small adaptations, and it introduces the concept of changing conditions into the task of referring expression generation. In this paper we describe the design of an algorithm that takes these aspects into account in order to create descriptions of people within a 3D virtual environment. The evaluation of this algorithm has shown that, by changing the descriptions in real time according to the observers point of view, they are able to identify the described person quickly and effectively.
BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking (2018)

The manual creation of gold standards for named entity recognition and entity linking is time- and resource-intensive. Moreover, recent works show that such gold standards contain a large proportion of mistakes in addition to being difficult to maintain. We hence present Bengal, a novel automatic generation of such gold standards as a complement to manually created benchmarks. The main advantage of our benchmarks is that they can be readily generated at any time. They are also cost-effective while being guaranteed to be free of annotation errors. We compare the performance of 11 tools on benchmarks in English generated by Bengal and on 16 benchmarks created manually. We show that our approach can be ported easily across languages by presenting results achieved by 4 tools on both Brazilian Portuguese and Spanish. Overall, our results suggest that our automatic benchmark generation approach can create varied benchmarks that have characteristics similar to those of existing benchmarks. Our approach is open-source. Our experimental results are available at \url{http://faturl.com/bengalexpinlg} and the code at \url{https://github.com/dice-group/BENGAL}.
Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem (2018)

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 {\textquotedblleft}packaged{\textquotedblright} 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.
Handling Rare Items in Data-to-Text Generation (2018)

Neural approaches to data-to-text generation generally handle rare input items using either delexicalisation or a copy mechanism. We investigate the relative impact of these two methods on two datasets (E2E and WebNLG) and using two evaluation settings. We show (i) that rare items strongly impact performance; (ii) that combining delexicalisation and copying yields the strongest improvement; (iii) that copying underperforms for rare and unseen items and (iv) that the impact of these two mechanisms greatly varies depending on how the dataset is constructed and on how it is split into train, dev and test.
Comprehension Driven Document Planning in Natural Language Generation Systems (2018)

This paper proposes an approach to NLG system design which focuses on generating output text which can be more easily processed by the reader. Ways in which cognitive theory might be combined with existing NLG techniques are discussed and two simple experiments in content ordering are presented.
Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study (2018)

Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS). However, due to the lack of large scale multi-document summaries, such methods can be hardly applied to multi-document summarization (MDS). In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS. We propose an approach to extend the neural abstractive model trained on large scale SDS data to the MDS task. Our approach only makes use of a small number of multi-document summaries for fine tuning. Experimental results on two benchmark DUC datasets demonstrate that our approach can outperform a variety of baseline neural models.
Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning (2018)

Developing conventional natural language generation systems requires extensive attention from human experts in order to craft complex sets of sentence planning rules. We propose a Bayesian nonparametric approach to learn sentence planning rules by inducing synchronous tree substitution grammars for pairs of text plans and morphosyntactically-specified dependency trees. Our system is able to learn rules which can be used to generate novel texts after training on small datasets.
The Task Matters: Comparing Image Captioning and Task-Based Dialogical Image Description (2018)

Image captioning models are typically trained on data that is collected from people who are asked to describe an image, without being given any further task context. As we argue here, this context independence is likely to cause problems for transferring to task settings in which image description is bound by task demands. We demonstrate that careful design of data collection is required to obtain image descriptions which are contextually bounded to a particular meta-level task. As a task, we use MeetUp!, a text-based communication game where two players have the goal of finding each other in a visual environment. To reach this goal, the players need to describe images representing their current location. We analyse a dataset from this domain and show that the nature of image descriptions found in MeetUp! is diverse, dynamic and rich with phenomena that are not present in descriptions obtained through a simple image captioning task, which we ran for comparison.
Generating Summaries of Sets of Consumer Products: Learning from Experiments (2018)

We explored the task of creating a textual summary describing a large set of objects characterised by a small number of features using an e-commerce dataset. When a set of consumer products is large and varied, it can be difficult for a consumer to understand how the products in the set differ; consequently, it can be challenging to choose the most suitable product from the set. To assist consumers, we generated high-level summaries of product sets. Two generation algorithms are presented, discussed, and evaluated with human users. Our evaluation results suggest a positive contribution to consumers' understanding of the domain.
Neural sentence generation from formal semantics (2018)

Sequence-to-sequence models have shown strong performance in a wide range of NLP tasks, yet their applications to sentence generation from logical representations are underdeveloped. In this paper, we present a sequence-to-sequence model for generating sentences from logical meaning representations based on event semantics. We use a semantic parsing system based on Combinatory Categorial Grammar (CCG) to obtain data annotated with logical formulas. We augment our sequence-to-sequence model with masking for predicates to constrain output sentences. We also propose a novel evaluation method for generation using Recognizing Textual Entailment (RTE). Combining parsing and generation, we test whether or not the output sentence entails the original text and vice versa. Experiments showed that our model outperformed a baseline with respect to both BLEU scores and accuracies in RTE.
Talking about other people: an endless range of possibilities (2018)

Image description datasets, such as Flickr30K and MS COCO, show a high degree of variation in the ways that crowd-workers talk about the world. Although this gives us a rich and diverse collection of data to work with, it also introduces uncertainty about how the world should be described. This paper shows the extent of this uncertainty in the PEOPLE-domain. We present a taxonomy of different ways to talk about other people. This taxonomy serves as a reference point to think about how other people should be described, and can be used to classify and compute statistics about labels applied to people.
Meteorologists and Students: A resource for language grounding of geographical descriptors (2018)

We present a data resource which can be useful for research purposes on language grounding tasks in the context of geographical referring expression generation. The resource is composed of two data sets that encompass 25 different geographical descriptors and a set of associated graphical representations, drawn as polygons on a map by two groups of human subjects: teenage students and expert meteorologists.
Cyclegen: Cyclic consistency based product review generator from attributes (2018)

In this paper we present an automatic review generator system which can generate personalized reviews based on the user identity, product identity and designated rating the user wishes to allot to the review. We combine this with a sentiment analysis system which performs the complimentary task of assigning ratings to reviews based purely on the textual content of the review. We introduce an additional loss term to ensure cyclic consistency of the sentiment rating of the generated review with the conditioning rating used to generate the review. The introduction of this new loss term constraints the generation space while forcing it to generate reviews adhering better to the requested rating. The use of {\textquoteleft}soft' generation and cyclic consistency allows us to train our model in an end to end fashion. We demonstrate the working of our model on product reviews from Amazon dataset.
Neural Transition-based Syntactic Linearization (2018)

The task of linearization is to find a grammatical order given a set of words. Traditional models use statistical methods. Syntactic linearization systems, which generate a sentence along with its syntactic tree, have shown state-of-the-art performance. Recent work shows that a multilayer LSTM language model outperforms competitive statistical syntactic linearization systems without using syntax. In this paper, we study neural syntactic linearization, building a transition-based syntactic linearizer leveraging a feed forward neural network, observing significantly better results compared to LSTM language models on this task.
Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs (2018)

One of the biggest challenges of end-to-end language generation from meaning representations in dialogue systems is making the outputs more natural and varied. Here we take a large corpus of 50K crowd-sourced utterances in the restaurant domain and develop text analysis methods that systematically characterize types of sentences in the training data. We then automatically label the training data to allow us to conduct two kinds of experiments with a neural generator. First, we test the effect of training the system with different stylistic partitions and quantify the effect of smaller, but more stylistically controlled training data. Second, we propose a method of labeling the style variants during training, and show that we can modify the style of the generated utterances using our stylistic labels. We contrast and compare these methods that can be used with any existing large corpus, showing how they vary in terms of semantic quality and stylistic control.
Char2char Generation with Reranking for the E2E NLG Challenge (2018)

This paper describes our submission to the E2E NLG Challenge. Recently, neural seq2seq approaches have become mainstream in NLG, often resorting to pre- (respectively post-) processing \textit{delexicalization} (relexicalization) steps at the word-level to handle rare words. By contrast, we train a simple character level seq2seq model, which requires no pre/post-processing (delexicalization, tokenization or even lowercasing), with surprisingly good results. For further improvement, we explore two re-ranking approaches for scoring candidates. We also introduce a synthetic dataset creation procedure, which opens up a new way of creating artificial datasets for Natural Language Generation.
E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language (2018)

In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data. An added challenge is to generate utterances that contain an accurate representation of the input, while reflecting the fluency and variety of human-generated text. In this paper, we report experiments with NLG models that can be used in task oriented dialogue systems. We explore the use of additional input to the model to encourage diversity and control of outputs. While our submission does not rank highly using automated metrics, qualitative investigation of generated utterances suggests the use of additional information in neural network NLG systems to be a promising research direction.
E2E NLG Challenge: Neural Models vs. Templates (2018)

E2E NLG Challenge is a shared task on generating restaurant descriptions from sets of key-value pairs. This paper describes the results of our participation in the challenge. We develop a simple, yet effective neural encoder-decoder model which produces fluent restaurant descriptions and outperforms a strong baseline. We further analyze the data provided by the organizers and conclude that the task can also be approached with a template-based model developed in just a few hours.
The E2E NLG Challenge: A Tale of Two Systems (2018)

This paper presents the two systems we entered into the 2017 E2E NLG Challenge: TemplGen, a templated-based system and SeqGen, a neural network-based system. Through the automatic evaluation, SeqGen achieved competitive results compared to the template-based approach and to other participating systems as well. In addition to the automatic evaluation, in this paper we present and discuss the human evaluation results of our two systems.
Interactive health insight miner: an adaptive, semantic-based approach (2018)

E-health applications aim to support the user in adopting healthy habits. An important feature is to provide insights into the user`s lifestyle. To actively engage the user in the insight mining process, we propose an ontology-based framework with a Controlled Natural Language interface, which enables the user to ask for specific insights and to customize personal information.
Multi-Language Surface Realisation as REST API based NLG Microservice (2018)

We present a readily available API that solves the morphology component for surface realizers in 10 languages (e.g., English, German and Finnish) for any topic and is available as REST API. This can be used to add morphology to any kind of NLG application (e.g., a multi-language chatbot), without requiring computational linguistic knowledge by the integrator.
Statistical NLG for Generating the Content and Form of Referring Expressions (2018)

This paper argues that a new generic approach to statistical NLG can be made to perform Referring Expression Generation (REG) successfully. The model does not only select attributes and values for referring to a target referent, but also performs Linguistic Realisation, generating an actual Noun Phrase. Our evaluations suggest that the attribute selection aspect of the algorithm exceeds classic REG algorithms, while the Noun Phrases generated are as similar to those in a previously developed corpus as were Noun Phrases produced by a new set of human speakers.
Specificity measures and reference (2018)

In this paper we study empirically the validity of measures of referential success for referring expressions involving gradual properties. More specifically, we study the ability of several measures of referential success to predict the success of a user in choosing the right object, given a referring expression. Experimental results indicate that certain fuzzy measures of success are able to predict human accuracy in reference resolution. Such measures are therefore suitable for the estimation of the success or otherwise of a referring expression produced by a generation algorithm, especially in case the properties in a domain cannot be assumed to have crisp denotations.