pwc-archive/papers-with-abstracts huggingface dataset:
Biomedical Relation Classification by single and multiple source domain adaptation
Authors: Sinchani Chakraborty, Sudeshna Sarkar, Pawan Goyal, Mahan Gattu, eeshwar
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Classification, Domain Adaptation, General Classification, Relation, Relation Classification

Relation classification is crucial for inferring semantic relatedness between entities in a piece of text. These systems can be trained given labelled data. However, relation classification is very domain-specific and it takes a lot of effort to label data for a new domain. In this paper, we explore domain adaptation techniques for this task. While past works have focused on single source domain adaptation for bio-medical relation classification, we classify relations in an unlabeled target domain by transferring useful knowledge from one or more related source domains. Our experiments with the model have shown to improve state-of-the-art F1 score on 3 benchmark biomedical corpora for single domain and on 2 out of 3 for multi-domain scenarios. When used with contextualized embeddings, there is further boost in performance outperforming neural-network based domain adaptation baselines for both the cases.
What does the language of foods say about us?
Authors: Hoang Van, Ahmad Musa, Hang Chen, Stephen Kobourov, Mihai Surdeanu
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

In this work we investigate the signal contained in the language of food on social media. We experiment with a dataset of 24 million food-related tweets, and make several observations. First,thelanguageoffoodhaspredictive power. We are able to predict if states in the United States (US) are above the medianratesfortype2diabetesmellitus(T2DM), income, poverty, and education {--} outperforming previous work by 4{--}18{\%}. Second, we investigate the effect of socioeconomic factors (income, poverty, and education) on predicting state-level T2DM rates. Socioeconomic factors do improve T2DM prediction, with the greatestimprovementcomingfrompovertyinformation(6{\%}),but,importantly,thelanguage of food adds distinct information that is not captured by socioeconomics. Third, we analyze how the language of food has changed over a five-year period (2013 {--} 2017), which is indicative of the shift in eating habits in the US during that period. We find several food trends, and that the language of food is used differently by different groups such as differentgenders. Last,weprovideanonlinevisualization tool for real-time queries and semantic analysis.
Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text
Authors: Hanna Berg, Taridzo Chomutare, Hercules Dalianis
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Benchmarking, De-identification

This article presents experiments with pseudonymised Swedish clinical text used as training data to de-identify real clinical text with the future aim to transfer non-sensitive training data to other hospitals. Conditional Random Fields (CFR) and Long Short-Term Memory (LSTM) machine learning algorithms were used to train de-identification models. The two models were trained on pseudonymised data and evaluated on real data. For benchmarking, models were also trained on real data, and evaluated on real data as well as trained on pseudonymised data and evaluated on pseudonymised data. CRF showed better performance for some PHI information like Date Part, First Name and Last Name; consistent with some reports in the literature. In contrast, poor performances on Location and Health Care Unit information were noted, partially due to the constrained vocabulary in the pseudonymised training data. It is concluded that it is possible to train transferable models based on pseudonymised Swedish clinical data, but even small narrative and distributional variation could negatively impact performance.
Automatic rubric-based content grading for clinical notes
Authors: Wen-wai Yim, Ashley Mills, Harold Chun, Teresa Hashiguchi, Justin Yew, Bryan Lu
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

Clinical notes provide important documentation critical to medical care, as well as billing and legal needs. Too little information degrades quality of care; too much information impedes care. Training for clinical note documentation is highly variable, depending on institutions and programs. In this work, we introduce the problem of automatic evaluation of note creation through rubric-based content grading, which has the potential for accelerating and regularizing clinical note documentation training. To this end, we describe our corpus creation methods as well as provide simple feature-based and neural network baseline systems. We further provide tagset and scaling experiments to inform readers of plausible expected performances. Our baselines show promising results with content point accuracy and kappa values at 0.86 and 0.71 on the test set.
Dilated LSTM with attention for Classification of Suicide Notes
Authors: Annika M Schoene, George Lacey, Alex Turner, er P, Nina Dethlefs
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Classification, General Classification

In this paper we present a dilated LSTM with attention mechanism for document-level classification of suicide notes, last statements and depressed notes. We achieve an accuracy of 87.34{\%} compared to competitive baselines of 80.35{\%} (Logistic Model Tree) and 82.27{\%} (Bi-directional LSTM with Attention). Furthermore, we provide an analysis of both the grammatical and thematic content of suicide notes, last statements and depressed notes. We find that the use of personal pronouns, cognitive processes and references to loved ones are most important. Finally, we show through visualisations of attention weights that the Dilated LSTM with attention is able to identify the same distinguishing features across documents as the linguistic analysis.
Recognizing UMLS Semantic Types with Deep Learning
Authors: Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Deep Learning, Entity Linking, Relation Extraction, Unsupervised Pre-training, Word Embeddings

Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of-the-art entity recognition approaches on a newly released dataset, MedMentions. This dataset contains over 4000 biomedical abstracts, annotated for UMLS semantic types. In comparison to existing datasets, MedMentions contains a far greater number of entity types, and thus represents a more challenging but realistic scenario in a real-world setting. We explore a number of relevant dimensions, including the use of contextual versus non-contextual word embeddings, general versus domain-specific unsupervised pre-training, and different deep learning architectures. We contrast our results against the well-known i2b2 2010 entity recognition dataset, and propose a new method to combine general and domain-specific information. While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0.90), our results on MedMentions are significantly lower (F1 = 0.63), suggesting there is still plenty of opportunity for improvement on this new data.
Ontological attention ensembles for capturing semantic concepts in ICD code prediction from clinical text
Authors: Matus Falis, Maciej Pajak, Aneta Lisowska, Patrick Schrempf, Lucas Deckers, Shadia Mikhael, Sotirios Tsaftaris, Alison O{'}Neil
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

We present a semantically interpretable system for automated ICD coding of clinical text documents. Our contribution is an ontological attention mechanism which matches the structure of the ICD ontology, in which shared attention vectors are learned at each level of the hierarchy, and combined into label-dependent ensembles. Analysis of the attention heads shows that shared concepts are learned by the lowest common denominator node. This allows child nodes to focus on the differentiating concepts, leading to efficient learning and memory usage. Visualisation of the multi-level attention on the original text allows explanation of the code predictions according to the semantics of the ICD ontology. On the MIMIC-III dataset we achieve a 2.7{\%} absolute (11{\%} relative) improvement from 0.218 to 0.245 macro-F1 score compared to the previous state of the art across 3,912 codes. Finally, we analyse the labelling inconsistencies arising from different coding practices which limit performance on this task.
Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text
Authors: Elena Sergeeva, Henghui Zhu, Amir Tahmasebi, Peter Szolovits
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Negation, Sentence

Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there has been numerous reports on improved performance on a variety of natural language tasks. Nevertheless, the degree to which the resulting context-aware representations encode information about morpho-syntactic properties of the word/token in a sentence remains unclear. In this paper, we investigate the application and impact of state-of-the-art neural token representations for automatic cue-conditional speculation and negation scope detection coupled with the independently computed morpho-syntactic information. Through this work, We establish a new state-of-the-art for the BioScope and NegPar corpus. More importantly, we provide a thorough analysis of neural representations and additional features interactions, cue-representation for conditioning, discuss model behavior on different datasets and address the annotation-induced biases in the learned representations.
The Second Multilingual Surface Realisation Shared Task (SR'19): Overview and Evaluation Results
Authors: Simon Mille, Anja Belz, Bernd Bohnet, Yvette Graham, Leo Wanner
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

We report results from the SR{'}19 Shared Task, the second edition of a multilingual surface realisation task organised as part of the EMNLP{'}19 Workshop on Multilingual Surface Realisation. As in SR{'}18, the shared task comprised two tracks with different levels of complexity: (a) a shallow track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (b) a deep track where additionally, functional words and morphological information were removed. The shallow track was offered in eleven, and the deep track in three languages. Systems were evaluated (a) automatically, using a range of intrinsic metrics, and (b) by human judges in terms of readability and meaning similarity. This report presents the evaluation results, along with descriptions of the SR{'}19 tracks, data and evaluation methods. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.
Learning to Order Graph Elements with Application to Multilingual Surface Realization
Authors: Wenchao Du, Alan W. black
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Combinatorial Optimization, Decoder, Graph Neural Network

Recent advances in deep learning have shown promises in solving complex combinatorial optimization problems, such as sorting variable-sized sequences. In this work, we take a step further and tackle the problem of ordering the elements of sequences that come with graph structures. Our solution adopts an encoder-decoder framework, in which the encoder is a graph neural network that learns the representation for each element, and the decoder predicts the ordering of each local neighborhood of the graph in turn. We apply our framework to multilingual surface realization, which is the task of ordering and completing sentences with their dependency parses given but without the ordering of words. Experiments show that our approach is much better for this task than prior works that do not consider graph structures. We participated in 2019 Surface Realization Shared Task (SR{'}19), and we ranked second out of 14 teams while outperforming those teams below by a large margin.
DepDist: Surface realization via regex and learned dependency-distance tolerance
Authors: William Dyer
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

This paper describes a method of inflecting and linearizing a lemmatized dependency tree by: (1) determining a regular expression and substitution to describe each productive wordform rule; (2) learning the dependency distance tolerance for each head-dependent pair, resulting in an edge-weighted directed acyclic graph (DAG); and (3) topologically sorting the DAG into a surface realization based on edge weight. The method{'}s output for 11 languages across 18 treebanks is competitive with the other submissions to the Second Multilingual Surface Realization Shared Task (SR {`}19).
BME-UW at SRST-2019: Surface realization with Interpreted Regular Tree Grammars
Authors: {\'A}d{\'a}m Kov{\'a}cs, Evelin {\'A}cs, Judit {\'A}cs, Andras Kornai, G{\'a}bor Recski
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Decoder, Semantic Parsing

The Surface Realization Shared Task involves mapping Universal Dependency graphs to raw text, i.e. restoring word order and inflection from a graph of typed, directed dependencies between lemmas. Interpreted Regular Tree Grammars (IRTGs) encode the correspondence between generations in multiple algebras, and have previously been used for semantic parsing from raw text. Our system induces an IRTG for simultaneously building pairs of surface forms and UD graphs in the SRST training data, then prunes this grammar for each UD graph in the test data for efficient parsing and generation of the surface ordering of lemmas. For the inflection step we use a standard sequence-to-sequence model with a biLSTM encoder and an LSTM decoder with attention. Both components of our system are available on GitHub under an MIT license.
Realizing Universal Dependencies Structures
Authors: Guy Lapalme
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Sentence

We first describe a surface realizer forUniversal Dependencies (UD) structures. The system uses a symbolic approach to transform the dependency tree into a tree of constituents that is transformed into an English sentence by an existing realizer. This approach was then adapted for the two shared tasks of SR{'}19. The system is quite fast and showed competitive results for English sentences using automatic and manual evaluation measures.
IMSurReal: IMS at the Surface Realization Shared Task 2019
Authors: Xiang Yu, Agnieszka Falenska, Marina Haid, Ngoc Thang Vu, Jonas Kuhn
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

We introduce the IMS contribution to the Surface Realization Shared Task 2019. Our submission achieves the state-of-the-art performance without using any external resources. The system takes a pipeline approach consisting of five steps: linearization, completion, inflection, contraction, and detokenization. We compare the performance of our linearization algorithm with two external baselines and report results for each step in the pipeline. Furthermore, we perform detailed error analysis revealing correlation between word order freedom and difficulty of the linearization task.
Surface Realization Shared Task 2019 (MSR19): The Team 6 Approach
Authors: Thiago Castro Ferreira, Emiel Krahmer
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Translation, Translation

This study describes the approach developed by the Tilburg University team to the shallow track of the Multilingual Surface Realization Shared Task 2019 (SR{'}19) (Mille et al., 2019). Based on Ferreira et al. (2017) and on our 2018 submission Ferreira et al. (2018), the approach generates texts by first preprocessing an input dependency tree into an ordered linearized string, which is then realized using a rule-based and a statistical machine translation (SMT) model. This year our submission is able to realize texts in the 11 languages proposed for the task, different from our last year submission, which covered only 6 Indo-European languages. The model is publicly available.
The Concordia NLG Surface Realizer at SRST 2019
Authors: Farhood Farahnak, Laya Rafiee, Leila Kosseim, Thomas Fevens
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Decoder

This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task. The model reconstructs sentences whose word order and word inflections were removed. We divided the problem into two sub-problems: reordering and inflecting. For the purpose of reordering, we used a pointer network integrated with a transformer model as its encoder-decoder modules. In order to generate the inflected forms of tokens, a Feed Forward Neural Network was employed.
The OSU/Facebook Realizer for SRST 2019: Seq2Seq Inflection and Serialized Tree2Tree Linearization
Authors: Kartikeya Upasani, David King, Jinfeng Rao, Anusha Balakrishnan, Michael White
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Morphological Inflection, Reranking, valid

We describe our exploratory system for the shallow surface realization task, which combines morphological inflection using character sequence-to-sequence models with a baseline linearizer that implements a tree-to-tree model using sequence-to-sequence models on serialized trees. Results for morphological inflection were competitive across languages. Due to time constraints, we could only submit complete results (including linearization) for English. Preliminary linearization results were decent, with a small benefit from reranking to prefer valid output trees, but inadequate control over the words in the output led to poor quality on longer sentences.
Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders
Authors: Xudong Hong, Ernie Chang, Vera Demberg
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Data Augmentation, Text Generation

The Multilingual Surface Realization Shared Task 2019 focuses on generating sentences from lemmatized sets of universal dependency parses with rich features. This paper describes the results of our participation in the deep track. The core innovation in our approach is to use a graph convolutional network to encode the dependency trees given as input. Upon adding morphological features, our system achieves the third rank without using data augmentation techniques or additional components (such as a re-ranker).
The DipInfoUniTo Realizer at SRST'19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization
Authors: Aless Mazzei, ro, Valerio Basile
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: BIG-bench Machine Learning, Learning-To-Rank, Prediction

We describe the system presented at the SR{'}19 shared task by the DipInfoUnito team. Our approach is based on supervised machine learning. In particular, we divide the SR task into two independent subtasks, namely word order prediction and morphology inflection prediction. Two neural networks with different architectures run on the same input structure, each producing a partial output which is recombined in the final step in order to produce the predicted surface form. This work is a direct successor of the architecture presented at SR{'}19.
LORIA / Lorraine University at Multilingual Surface Realisation 2019
Authors: Anastasia Shimorina, Claire Gardent
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

This paper presents the LORIA / Lorraine University submission at the Multilingual Surface Realisation shared task 2019 for the shallow track. We outline our approach and evaluate it on 11 languages covered by the shared task. We provide a separate evaluation of each component of our pipeline, concluding on some difficulties and suggesting directions for future work.
Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations
Authors: Marco Antonio Sobrevilla Cabezudo, Simon Mille, Thiago Pardo
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Abstract Meaning Representation, Machine Translation, Text Generation, Translation

This paper presents an exploratory study that aims to evaluate the usefulness of back-translation in Natural Language Generation (NLG) from semantic representations for non-English languages. Specifically, Abstract Meaning Representation and Brazilian Portuguese (BP) are chosen as semantic representation and language, respectively. Two methods (focused on Statistical and Neural Machine Translation) are evaluated on two datasets (one automatically generated and another one human-generated) to compare the performance in a real context. Also, several cuts according to quality measures are performed to evaluate the importance (or not) of the data quality in NLG. Results show that there are still many improvements to be made but this is a promising approach.
Big Generalizations with Small Data: Exploring the Role of Training Samples in Learning Adjectives of Size
Authors: S Pezzelle, ro, Raquel Fern{\'a}ndez
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Small Data Image Classification, Visual Reasoning

In this paper, we experiment with a recently proposed visual reasoning task dealing with quantities {--} modeling the multimodal, contextually-dependent meaning of size adjectives ({`}big{'}, {`}small{'}) {--} and explore the impact of varying the training data on the learning behavior of a state-of-art system. In previous work, models have been shown to fail in generalizing to unseen adjective-noun combinations. Here, we investigate whether, and to what extent, seeing some of these cases during training helps a model understand the rule subtending the task, i.e., that being big implies being not small, and vice versa. We show that relatively few examples are enough to understand this relationship, and that developing a specific, mutually exclusive representation of size adjectives is beneficial to the task.
Eigencharacter: An Embedding of Chinese Character Orthography
Authors: Yu-Hsiang Tseng, Shu-Kai Hsieh
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: World Knowledge

Chinese characters are unique in its logographic nature, which inherently encodes world knowledge through thousands of years evolution. This paper proposes an embedding approach, namely eigencharacter (EC) space, which helps NLP application easily access the knowledge encoded in Chinese orthography. These EC representations are automatically extracted, encode both structural and radical information, and easily integrate with other computational models. We built EC representations of 5,000 Chinese characters, investigated orthography knowledge encoded in ECs, and demonstrated how these ECs identified visually similar characters with both structural and radical information.
On the Role of Scene Graphs in Image Captioning
Authors: Dalin Wang, Daniel Beck, Trevor Cohn
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Descriptive, Image Captioning

Scene graphs represent semantic information in images, which can help image captioning system to produce more descriptive outputs versus using only the image as context. Recent captioning approaches rely on ad-hoc approaches to obtain graphs for images. However, those graphs introduce noise and it is unclear the effect of parser errors on captioning accuracy. In this work, we investigate to what extent scene graphs can help image captioning. Our results show that a state-of-the-art scene graph parser can boost performance almost as much as the ground truth graphs, showing that the bottleneck currently resides more on the captioning models than on the performance of the scene graph parser.