pwc-archive/papers-with-abstracts huggingface dataset:
Addressing Low-Resource Scenarios with Character-aware Embeddings
Authors: Sean Papay, Sebastian Pad{\'o}, Ngoc Thang Vu
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Morphological Analysis, Word Embeddings

Most modern approaches to computing word embeddings assume the availability of text corpora with billions of words. In this paper, we explore a setup where only corpora with millions of words are available, and many words in any new text are out of vocabulary. This setup is both of practical interests {--} modeling the situation for specific domains and low-resource languages {--} and of psycholinguistic interest, since it corresponds much more closely to the actual experiences and challenges of human language learning and use. We compare standard skip-gram word embeddings with character-based embeddings on word relatedness prediction. Skip-grams excel on large corpora, while character-based embeddings do well on small corpora generally and rare and complex words specifically. The models can be combined easily.
Subword-level Composition Functions for Learning Word Embeddings
Authors: Bofang Li, Aleks Drozd, R, Tao Liu, Xiaoyong Du
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Learning Word Embeddings, Word Embeddings

Subword-level information is crucial for capturing the meaning and morphology of words, especially for out-of-vocabulary entries. We propose CNN- and RNN-based subword-level composition functions for learning word embeddings, and systematically compare them with popular word-level and subword-level models (Skip-Gram and FastText). Additionally, we propose a hybrid training scheme in which a pure subword-level model is trained jointly with a conventional word-level embedding model based on lookup-tables. This increases the fitness of all types of subword-level word embeddings; the word-level embeddings can be discarded after training, leaving only compact subword-level representation with much smaller data volume. We evaluate these embeddings on a set of intrinsic and extrinsic tasks, showing that subword-level models have advantage on tasks related to morphology and datasets with high OOV rate, and can be combined with other types of embeddings.
Discovering Phonesthemes with Sparse Regularization
Authors: Nelson F. Liu, Gina-Anne Levow, Noah A. Smith
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: feature selection

We introduce a simple method for extracting non-arbitrary form-meaning representations from a collection of semantic vectors. We treat the problem as one of feature selection for a model trained to predict word vectors from subword features. We apply this model to the problem of automatically discovering phonesthemes, which are submorphemic sound clusters that appear in words with similar meaning. Many of our model-predicted phonesthemes overlap with those proposed in the linguistics literature, and we validate our approach with human judgments.
Meaningless yet meaningful: Morphology grounded subword-level NMT
Authors: Tamali Banerjee, Pushpak Bhattacharyya
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Machine Translation, NMT, Segmentation, Translation, Transliteration

We explore the use of two independent subsystems Byte Pair Encoding (BPE) and Morfessor as basic units for subword-level neural machine translation (NMT). We show that, for linguistically distant language-pairs Morfessor-based segmentation algorithm produces significantly better quality translation than BPE. However, for close language-pairs BPE-based subword-NMT may translate better than Morfessor-based subword-NMT. We propose a combined approach of these two segmentation algorithms Morfessor-BPE (M-BPE) which outperforms these two baseline systems in terms of BLEU score. Our results are supported by experiments on three language-pairs: English-Hindi, Bengali-Hindi and English-Bengali.
Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings
Authors: Hrishikesh Ganu, Viswa Datha P.
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Information Retrieval, Lemmatization, Word Embeddings

We present early results from a system under development which uses sub-word embeddings for query expansion in presence of mis-spelled words and other aberrations. We work for a company which creates accounting software and the end goal is to improve customer experience when they search for help on our {``}Customer Care{''} portal. Our customers use colloquial language, non-standard acronyms and sometimes mis-spell words when they use our Search portal or interact over other channels. However, our Knowledge Base has curated content which leverages technical terms and is in language which is quite formal. This results in the answer not being retrieved even though the answer might actually be present in the documentation (as assessed by a human). We address this problem by creating equivalence classes of words with similar meanings (with the additional property that the mappings to these equivalence classes are robust to mis-spellings) using sub-word embeddings and then use them to fine tune an Elasticsearch index to improve recall. We demonstrate through an end-end system that using sub-word embeddings leads to a significant lift in correct answers retrieved for an accounting corpus available in the public domain.
A Multi-Context Character Prediction Model for a Brain-Computer Interface
Authors: Shiran Dudy, Shaobin Xu, Steven Bedrick, David Smith
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Language Modeling, Language Modelling

Brain-computer interfaces and other augmentative and alternative communication devices introduce language-modeing challenges distinct from other character-entry methods. In particular, the acquired signal of the EEG (electroencephalogram) signal is noisier, which, in turn, makes the user intent harder to decipher. In order to adapt to this condition, we propose to maintain ambiguous history for every time step, and to employ, apart from the character language model, word information to produce a more robust prediction system. We present preliminary results that compare this proposed Online-Context Language Model (OCLM) to current algorithms that are used in this type of setting. Evaluation on both perplexity and predictive accuracy demonstrates promising results when dealing with ambiguous histories in order to provide to the front end a distribution of the next character the user might type.
Using Hedge Detection to Improve Committed Belief Tagging
Authors: Morgan Ulinski, Seth Benjamin, Julia Hirschberg
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: General Classification, Sentence Classification

We describe a novel method for identifying hedge terms using a set of manually constructed rules. We present experiments adding hedge features to a committed belief system to improve classification. We compare performance of this system (a) without hedging features, (b) with dictionary-based features, and (c) with rule-based features. We find that using hedge features improves performance of the committed belief system, particularly in identifying instances of non-committed belief and reported belief.
Paths for uncertainty: Exploring the intricacies of uncertainty identification for news
Authors: Chrysoula Zerva, Sophia Ananiadou
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Articles

Currently, news articles are produced, shared and consumed at an extremely rapid rate. Although their quantity is increasing, at the same time, their quality and trustworthiness is becoming fuzzier. Hence, it is important not only to automate information extraction but also to quantify the certainty of this information. Automated identification of certainty has been studied both in the scientific and newswire domains, but performance is considerably higher in tasks focusing on scientific text. We compare the differences in the definition and expression of uncertainty between a scientific domain, i.e., biomedicine, and newswire. We delve into the different aspects that affect the certainty of an extracted event in a news article and examine whether they can be easily identified by techniques already validated in the biomedical domain. Finally, we present a comparison of the syntactic and lexical differences between the the expression of certainty in the biomedical and newswire domains, using two annotated corpora.
Detecting Sarcasm is Extremely Easy ;-)
Authors: Natalie Parde, Rodney Nielsen
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Sarcasm Detection

Detecting sarcasm in text is a particularly challenging problem in computational semantics, and its solution may vary across different types of text. We analyze the performance of a domain-general sarcasm detection system on datasets from two very different domains: Twitter, and Amazon product reviews. We categorize the errors that we identify with each, and make recommendations for addressing these issues in NLP systems in the future.
GKR: the Graphical Knowledge Representation for semantic parsing
Authors: Aikaterini-Lida Kalouli, Richard Crouch
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Natural Language Inference, Semantic Parsing, Semantic Similarity, Semantic Textual Similarity, Sentence

This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e.g. for natural language inference, reasoning and semantic similarity. The Graphical Knowledge Representation which is output by the parser is inspired by the Abstract Knowledge Representation, which separates out conceptual and contextual levels of representation that deal respectively with the subject matter of a sentence and its existential commitments. Our representation is a layered graph with each sub-graph holding different kinds of information, including one sub-graph for concepts and one for contexts. Our first evaluation of the system shows an F-score of 85{\%} in accurately representing sentences as semantic graphs.
Computational Argumentation: A Journey Beyond Semantics, Logic, Opinions, and Easy Tasks
Authors: Ivan Habernal
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Common Sense Reasoning

The classical view on argumentation, such that arguments are logical structures consisting of different distinguishable parts and that parties exchange arguments in a rational way, is prevalent in textbooks but nonexistent in the real world. Instead, argumentation is a multifaceted communication tool built upon humans{'} capabilities to easily use common sense, emotions, and social context. As humans, we are pretty good at it. Computational Argumentation tries to tackle these phenomena but has a long and not so easy way to go. In this talk, I would like to shed a light on several recent attempts to deal with argumentation computationally, such as addressing argument quality, understanding argument reasoning, dealing with fallacies, and how should we never ever argue online.
Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models
Authors: Simon Dobnik, Mehdi Ghanimifard, John Kelleher
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Image Captioning

The challenge for computational models of spatial descriptions for situated dialogue systems is the integration of information from different modalities. The semantics of spatial descriptions are grounded in at least two sources of information: (i) a geometric representation of space and (ii) the functional interaction of related objects that. We train several neural language models on descriptions of scenes from a dataset of image captions and examine whether the functional or geometric bias of spatial descriptions reported in the literature is reflected in the estimated perplexity of these models. The results of these experiments have implications for the creation of models of spatial lexical semantics for human-robot dialogue systems. Furthermore, they also provide an insight into the kinds of the semantic knowledge captured by neural language models trained on spatial descriptions, which has implications for image captioning systems.
Building and Learning Structures in a Situated Blocks World Through Deep Language Understanding
Authors: Ian Perera, James Allen, Choh Man Teng, Lucian Galescu
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6

We demonstrate a system for understanding natural language utterances for structure description and placement in a situated blocks world context. By relying on a rich, domain-specific adaptation of a generic ontology and a logical form structure produced by a semantic parser, we obviate the need for an intermediate, domain-specific representation and can produce a reasoner that grounds and reasons over concepts and constraints with real-valued data. This linguistic base enables more flexibility in interpreting natural language expressions invoking intrinsic concepts and features of structures and space. We demonstrate some of the capabilities of a system grounded in deep language understanding and present initial results in a structure learning task.
Computational Models for Spatial Prepositions
Authors: Georgiy Platonov, Lenhart Schubert
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6

Developing computational models of spatial prepositions (such as on, in, above, etc.) is crucial for such tasks as human-machine collaboration, story understanding, and 3D model generation from descriptions. However, these prepositions are notoriously vague and ambiguous, with meanings depending on the types, shapes and sizes of entities in the argument positions, the physical and task context, and other factors. As a result truth value judgments for prepositional relations are often uncertain and variable. In this paper we treat the modeling task as calling for assignment of probabilities to such relations as a function of multiple factors, where such probabilities can be viewed as estimates of whether humans would judge the relations to hold in given circumstances. We implemented our models in a 3D blocks world and a room world in a computer graphics setting, and found that true/false judgments based on these models do not differ much more from human judgments that the latter differ from one another. However, what really matters pragmatically is not the accuracy of truth value judgments but whether, for instance, the computer models suffice for identifying objects described in terms of prepositional relations, (e.g., {``}the box to the left of the table{''}, where there are multiple boxes). For such tasks, our models achieved accuracies above 90{\%} for most relations.
Lexical Conceptual Structure of Literal and Metaphorical Spatial Language: A Case Study of ``Push''
Authors: Bonnie Dorr, Mari Olsen
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Machine Translation, Translation, Visual Reasoning

Prior methodologies for understanding spatial language have treated literal expressions such as {``}Mary pushed the car over the edge{''} differently from metaphorical extensions such as {``}Mary{'}s job pushed her over the edge{''}. We demonstrate a methodology for standardizing literal and metaphorical meanings, by building on work in Lexical Conceptual Structure (LCS), a general-purpose representational component used in machine translation. We argue that spatial predicates naturally extend into other fields (e.g., circumstantial or temporal), and that LCS provides both a framework for distinguishing spatial from non-spatial, and a system for finding metaphorical meaning extensions. We start with MetaNet (MN), a large repository of conceptual metaphors, condensing 197 spatial entries into sixteen top-level categories of motion frames. Using naturally occurring instances of English push , and expansions of MN frames, we demonstrate that literal and metaphorical extensions exhibit patterns predicted and represented by the LCS model.
Representing Spatial Relations in FrameNet
Authors: Miriam R. L. Petruck, Michael J. Ellsworth
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Position

While humans use natural language to express spatial relations between and across entities in the world with great facility, natural language systems have a facility that depends on that human facility. This position paper presents approach to representing spatial relations in language, and advocates its adoption for representing the meaning of spatial language. This work shows the importance of axis-orientation systems for capturing the complexity of spatial relations, which FrameNet encodes with semantic types.
Anaphora Resolution for Improving Spatial Relation Extraction from Text
Authors: Umar Manzoor, Parisa Kordjamshidi
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Relation, Relation Extraction

Spatial relation extraction from generic text is a challenging problem due to the ambiguity of the prepositions spatial meaning as well as the nesting structure of the spatial descriptions. In this work, we highlight the difficulties that the anaphora can make in the extraction of spatial relations. We use external multi-modal (here visual) resources to find the most probable candidates for resolving the anaphoras that refer to the landmarks of the spatial relations. We then use global inference to decide jointly on resolving the anaphora and extraction of the spatial relations. Our preliminary results show that resolving anaphora improves the state-of-the-art results on spatial relation extraction.
The Case for Systematically Derived Spatial Language Usage
Authors: Bonnie Dorr, Clare Voss
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Position, Robot Navigation

This position paper argues that, while prior work in spatial language understanding for tasks such as robot navigation focuses on mapping natural language into deep conceptual or non-linguistic representations, it is possible to systematically derive regular patterns of spatial language usage from existing lexical-semantic resources. Furthermore, even with access to such resources, effective solutions to many application areas such as robot navigation and narrative generation also require additional knowledge at the syntax-semantics interface to cover the wide range of spatial expressions observed and available to natural language speakers. We ground our insights in, and present our extensions to, an existing lexico-semantic resource, covering 500 semantic classes of verbs, of which 219 fall within a spatial subset. We demonstrate that these extensions enable systematic derivation of regular patterns of spatial language without requiring manual annotation.
Learning to Listen: Critically Considering the Role of AI in Human Storytelling and Character Creation
Authors: Anna Kasunic, Geoff Kaufman
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6

In this opinion piece, we argue that there is a need for alternative design directions to complement existing AI efforts in narrative and character generation and algorithm development. To make our argument, we a) outline the predominant roles and goals of AI research in storytelling; b) present existing discourse on the benefits and harms of narratives; and c) highlight the pain points in character creation revealed by semi-structured interviews we conducted with 14 individuals deeply involved in some form of character creation. We conclude by proffering several specific design avenues that we believe can seed fruitful research collaborations. In our vision, AI collaborates with humans during creative processes and narrative generation, helps amplify voices and perspectives that are currently marginalized or misrepresented, and engenders experiences of narrative that support spectatorship and listening roles.
Linguistic Features of Helpfulness in Automated Support for Creative Writing
Authors: Melissa Roemmele, Andrew Gordon
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Grammatical Error Correction, Story Generation, Text Generation

We examine an emerging NLP application that supports creative writing by automatically suggesting continuing sentences in a story. The application tracks users{'} modifications to generated sentences, which can be used to quantify their {``}helpfulness{''} in advancing the story. We explore the task of predicting helpfulness based on automatically detected linguistic features of the suggestions. We illustrate this analysis on a set of user interactions with the application using an initial selection of features relevant to story generation.
Telling Stories with Soundtracks: An Empirical Analysis of Music in Film
Authors: Jon Gillick, David Bamman
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Image Captioning, Question Answering

Soundtracks play an important role in carrying the story of a film. In this work, we collect a corpus of movies and television shows matched with subtitles and soundtracks and analyze the relationship between story, song, and audience reception. We look at the content of a film through the lens of its latent topics and at the content of a song through descriptors of its musical attributes. In two experiments, we find first that individual topics are strongly associated with musical attributes, and second, that musical attributes of soundtracks are predictive of film ratings, even after controlling for topic and genre.
Towards Controllable Story Generation
Authors: Nanyun Peng, Marjan Ghazvininejad, Jonathan May, Kevin Knight
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Story Generation, Text Generation

We present a general framework of analyzing existing story corpora to generate controllable and creative new stories. The proposed framework needs little manual annotation to achieve controllable story generation. It creates a new interface for humans to interact with computers to generate personalized stories. We apply the framework to build recurrent neural network (RNN)-based generation models to control story ending valence and storyline. Experiments show that our methods successfully achieve the control and enhance the coherence of stories through introducing storylines. with additional control factors, the generation model gets lower perplexity, and yields more coherent stories that are faithful to the control factors according to human evaluation.
An Encoder-decoder Approach to Predicting Causal Relations in Stories
Authors: Melissa Roemmele, Andrew Gordon
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Decoder

We address the task of predicting causally related events in stories according to a standard evaluation framework, the Choice of Plausible Alternatives (COPA). We present a neural encoder-decoder model that learns to predict relations between adjacent sequences in stories as a means of modeling causality. We explore this approach using different methods for extracting and representing sequence pairs as well as different model architectures. We also compare the impact of different training datasets on our model. In particular, we demonstrate the usefulness of a corpus not previously applied to COPA, the ROCStories corpus. While not state-of-the-art, our results establish a new reference point for systems evaluated on COPA, and one that is particularly informative for future neural-based approaches.
Neural Event Extraction from Movies Description
Authors: Alex Tozzo, Dejan Jovanovi{\'c}, Mohamed Amer
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Event Extraction, Machine Translation, Question Answering, Story Completion, Visual Storytelling

We present a novel approach for event extraction and abstraction from movie descriptions. Our event frame consists of {``}who{''}, {``}did what{''} {``}to whom{''}, {``}where{''}, and {``}when{''}. We formulate our problem using a recurrent neural network, enhanced with structural features extracted from syntactic parser, and trained using curriculum learning by progressively increasing the difficulty of the sentences. Our model serves as an intermediate step towards question answering systems, visual storytelling, and story completion tasks. We evaluate our approach on MovieQA dataset.