pwc-archive/papers-with-abstracts huggingface dataset:
Task Proposal: The TL;DR Challenge
Authors: Shahbaz Syed, Michael V{\"o}lske, Martin Potthast, Nedim Lipka, Benno Stein, Hinrich Sch{\"u}tze
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Abstractive Text Summarization, Information Retrieval, Text Generation, Text Summarization

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 {``}TL;DR{''}{---}for {``}too long; didn{'}t read{''}{---}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.
Adapting Descriptions of People to the Point of View of a Moving Observer
Authors: Gonzalo M{\'e}ndez, Raquel Herv{\'a}s, Pablo Gerv{\'a}s, Ricardo de la Rosa, Daniel Ruiz
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Position, Referring Expression, Referring expression generation, Text Generation

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.
Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem
Authors: Alex Shvets, er, Simon Mille, Leo Wanner
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Community Detection, Sentence, Text Generation, Text Simplification

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.
Handling Rare Items in Data-to-Text Generation
Authors: Anastasia Shimorina, Claire Gardent
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Data-to-Text Generation, KG-to-Text Generation, Text Generation

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
Authors: Craig Thomson, Ehud Reiter, Somayajulu Sripada
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Text Generation

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
Authors: Jianmin Zhang, Jiwei Tan, Xiaojun Wan
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Abstractive Text Summarization, Document Summarization, Machine Translation, Multi-Document Summarization, Text Generation

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
Authors: David M. Howcroft, Dietrich Klakow, Vera Demberg
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Sentence, Text Generation

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
Authors: Nikolai Ilinykh, Sina Zarrie{\ss}, David Schlangen
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Image Captioning, Image Description, Text Generation

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
Authors: Kittipitch Kuptavanich, Ehud Reiter, Kees Van Deemter, Advaith Siddharthan
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Text Generation

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
Authors: Kana Manome, Masashi Yoshikawa, Hitomi Yanaka, Pascual Mart{\'\i}nez-G{\'o}mez, Koji Mineshima, Daisuke Bekki
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Machine Translation, Natural Language Inference, RTE, Semantic Parsing, Semantic Textual Similarity, Sentence, Text Generation

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
Authors: Emiel van Miltenburg, Desmond Elliott, Piek Vossen
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Image Description, Text Generation

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.
Cyclegen: Cyclic consistency based product review generator from attributes
Authors: Vasu  Sharma, Harsh Sharma, Ankita Bishnu, Labhesh Patel
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Sentiment Analysis, Text Generation

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 {`}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.
E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language
Authors: Henry Elder, Sebastian Gehrmann, Alex O{'}Connor, er, Qun Liu
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Diversity, Machine Translation, Task-Oriented Dialogue Systems, Text Generation

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
Authors: Yevgeniy Puzikov, Iryna Gurevych
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Data-to-Text Generation, Decoder, Text Generation

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
Authors: Charese Smiley, Elnaz Davoodi, Dezhao Song, Frank Schilder
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Text Generation, Vocal Bursts Valence Prediction

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
Authors: Isabel Funke, Rim Helaoui, Aki H{\"a}rm{\"a}
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Common Sense Reasoning, Text Generation, Text Summarization

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
Authors: Andreas Madsack, Johanna Heininger, Nyamsuren Davaasambuu, Vitaliia Voronik, Michael K{\"a}ufl, Robert Wei{\ss}graeber
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Chatbot, Text Generation

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
Authors: Xiao Li, Kees Van Deemter, Chenghua Lin
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Attribute, Form, Referring Expression, Referring expression generation, Text Generation

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.
Decoding Strategies for Neural Referring Expression Generation
Authors: Sina Zarrie{\ss}, David Schlangen
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Image Captioning, Machine Translation, Referring Expression, Referring expression generation, Text Generation

RNN-based sequence generation is now widely used in NLP and NLG (natural language generation). Most work focusses on how to train RNNs, even though also decoding is not necessarily straightforward: previous work on neural MT found seq2seq models to radically prefer short candidates, and has proposed a number of beam search heuristics to deal with this. In this work, we assess decoding strategies for referring expression generation with neural models. Here, expression length is crucial: output should neither contain too much or too little information, in order to be pragmatically adequate. We find that most beam search heuristics developed for MT do not generalize well to referring expression generation (REG), and do not generally outperform greedy decoding. We observe that beam search heuristics for termination seem to override the model{'}s knowledge of what a good stopping point is. Therefore, we also explore a recent approach called trainable decoding, which uses a small network to modify the RNN{'}s hidden state for better decoding results. We find this approach to consistently outperform greedy decoding for REG.
Entropy Reduction correlates with temporal lobe activity
Authors: Matthew Nelson, Stanislas Dehaene, Christophe Pallier, John Hale
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

Using the Entropy Reduction incremental complexity metric, we relate high gamma power signals from the brains of epileptic patients to incremental stages of syntactic analysis in English and French. We find that signals recorded intracranially from the anterior Inferior Temporal Sulcus (aITS) and the posterior Inferior Temporal Gyrus (pITG) correlate with word-by-word Entropy Reduction values derived from phrase structure grammars for those languages. In the anterior region, this correlation persists even in combination with surprisal co-predictors from PCFG and ngram models. The result confirms the idea that the brain{'}s temporal lobe houses a parsing function, one whose incremental processing difficulty profile reflects changes in grammatical uncertainty.
Learning an Input Filter for Argument Structure Acquisition
Authors: Laurel Perkins, Naomi Feldman, Jeffrey Lidz
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

How do children learn a verb{'}s argument structure when their input contains nonbasic clauses that obscure verb transitivity? Here we present a new model that infers verb transitivity by learning to filter out non-basic clauses that were likely parsed in error. In simulations with child-directed speech, we show that this model accurately categorizes the majority of 50 frequent transitive, intransitive and alternating verbs, and jointly learns appropriate parameters for filtering parsing errors. Our model is thus able to filter out problematic data for verb learning without knowing in advance which data need to be filtered.
Grounding sound change in ideal observer models of perception
Authors: Zachary Burchill, T. Florian Jaeger
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

An important predictor of historical sound change, functional load, fails to capture insights from speech perception. Building on ideal observer models of word recognition, we devise a new definition of functional load that incorporates both a priori predictability and perceptual information. We explore this new measure with a simple model and find that it outperforms traditional measures.
``Oh, I've Heard That Before'': Modelling Own-Dialect Bias After Perceptual Learning by Weighting Training Data
Authors: Rachael Tatman
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Human listeners are able to quickly and robustly adapt to new accents and do so by using information about speaker{'}s identities. This paper will present experimental evidence that, even considering information about speaker{'}s identities, listeners retain a strong bias towards the acoustics of their own dialect after dialect learning. Participants{'} behaviour was accurately mimicked by a classifier which was trained on more cases from the base dialect and fewer from the target dialect. This suggests that imbalanced training data may result in automatic speech recognition errors consistent with those of speakers from populations over-represented in the training data.
Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation
Authors: Am Doucette, a
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

A recurrent neural network model of phonological pattern learning is proposed. The model is a relatively simple neural network with one recurrent layer, and displays biases in learning that mimic observed biases in human learning. Single-feature patterns are learned faster than two-feature patterns, and vowel or consonant-only patterns are learned faster than patterns involving vowels and consonants, mimicking the results of laboratory learning experiments. In non-recurrent models, capturing these biases requires the use of alpha features or some other representation of repeated features, but with a recurrent neural network, these elaborations are not necessary.
Predicting Japanese scrambling in the wild
Authors: Naho Orita
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: regression, Sentence

Japanese speakers have a choice between canonical SOV and scrambled OSV word order to express the same meaning. Although previous experiments examine the influence of one or two factors for scrambling in a controlled setting, it is not yet known what kinds of multiple effects contribute to scrambling. This study uses naturally distributed data to test the multiple effects on scrambling simultaneously. A regression analysis replicates the NP length effect and suggests the influence of noun types, but it provides no evidence for syntactic priming, given-new ordering, and the animacy effect. These findings only show evidence for sentence-internal factors, but we find no evidence that discourse level factors play a role.
Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion Annotation
Authors: Sven Buechel, Udo Hahn
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Reading Comprehension

We here examine how different perspectives of understanding written discourse, like the reader{'}s, the writer{'}s or the text{'}s point of view, affect the quality of emotion annotations. We conducted a series of annotation experiments on two corpora, a popular movie review corpus and a genre- and domain-balanced corpus of standard English. We found statistical evidence that the writer{'}s perspective yields superior annotation quality overall. However, the quality one perspective yields compared to the other(s) seems to depend on the domain the utterance originates from. Our data further suggest that the popular movie review data set suffers from an atypical bimodal distribution which may decrease model performance when used as a training resource.