pwc-archive/papers-with-abstracts huggingface dataset:
Metonymy Analysis Using Associative Relations between Words
Authors: Takehiro Teraoka
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentence

Metonymy is a figure of speech in which one item{'}s name represents another item that usually has a close relation with the first one. Metonymic expressions need to be correctly detected and interpreted because sentences including such expressions have different mean- ings from literal ones; computer systems may output inappropriate results in natural language processing. In this paper, an associative approach for analyzing metonymic expressions is proposed. By using associative information and two conceptual distances between words in a sentence, a previous method is enhanced and a decision tree is trained to detect metonymic expressions. After detecting these expressions, they are interpreted as metonymic understanding words by using associative information. This method was evaluated by comparing it with two baseline methods based on previous studies on the Japanese language that used case frames and co-occurrence information. As a result, the proposed method exhibited significantly better accuracy (0.85) of determining words as metonymic or literal expressions than the baselines. It also exhibited better accuracy (0.74) of interpreting the detected metonymic expressions than the baselines.
Embedding Open-domain Common-sense Knowledge from Text
Authors: Travis Goodwin, S Harabagiu, a
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Common Sense Reasoning, Open Information Extraction

Our ability to understand language often relies on common-sense knowledge â€• background information the speaker can assume is known by the reader. Similarly, our comprehension of the language used in complex domains relies on access to domain-specific knowledge. Capturing common-sense and domain-specific knowledge can be achieved by taking advantage of recent advances in open information extraction (IE) techniques and, more importantly, of knowledge embeddings, which are multi-dimensional representations of concepts and relations. Building a knowledge graph for representing common-sense knowledge in which concepts discerned from noun phrases are cast as vertices and lexicalized relations are cast as edges leads to learning the embeddings of common-sense knowledge accounting for semantic compositionality as well as implied knowledge. Common-sense knowledge is acquired from a vast collection of blogs and books as well as from WordNet. Similarly, medical knowledge is learned from two large sets of electronic health records. The evaluation results of these two forms of knowledge are promising: the same knowledge acquisition methodology based on learning knowledge embeddings works well both for common-sense knowledge and for medical knowledge Interestingly, the common-sense knowledge that we have acquired was evaluated as being less neutral than than the medical knowledge, as it often reflected the opinion of the knowledge utterer. In addition, the acquired medical knowledge was evaluated as more plausible than the common-sense knowledge, reflecting the complexity of acquiring common-sense knowledge due to the pragmatics and economicity of language.
Medical Concept Embeddings via Labeled Background Corpora
Authors: Eneldo Loza Menc{\'\i}a, Gerard de Melo, Jinseok Nam
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Word Similarity

In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things, these facilitate computing word similarity and relatedness scores. The most well-known example of algorithms to produce representations of this sort are the word2vec approaches. In this paper, we investigate a new model to induce such vector spaces for medical concepts, based on a joint objective that exploits not only word co-occurrences but also manually labeled documents, as available from sources such as PubMed. Our extensive experimental analysis shows that our embeddings lead to significantly higher correlations with human similarity and relatedness assessments than previous work. Due to the simplicity and versatility of vector representations, these findings suggest that our resource can easily be used as a drop-in replacement to improve any systems relying on medical concept similarity measures.
Question-Answering with Logic Specific to Video Games
Authors: Corentin Dumont, Ran Tian, Kentaro Inui
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Clustering, Minecraft, Question Answering

We present a corpus and a knowledge database aiming at developing Question-Answering in a new context, the open world of a video game. We chose a popular game called {`}Minecraft{'}, and created a QA corpus with a knowledge database related to this game and the ontology of a meaning representation that will be used to structure this database. We are interested in the logic rules specific to the game, which may not exist in the real world. The ultimate goal of this research is to build a QA system that can answer natural language questions from players by using inference on these game-specific logic rules. The QA corpus is partially composed of online quiz questions and partially composed of manually written variations of the most relevant ones. The knowledge database is extracted from several wiki-like websites about Minecraft. It is composed of unstructured data, such as text, that will be structured using the meaning representation we defined, and already structured data such as infoboxes. A preliminary examination of the data shows that players are asking creative questions about the game, and that the QA corpus can be used for clustering verbs and linking them to predefined actions in the game.
Mining the Spoken Wikipedia for Speech Data and Beyond
Authors: Arne K{\"o}hn, Florian Stegen, Timo Baumann
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, Sentence

We present a corpus of time-aligned spoken data of Wikipedia articles as well as the pipeline that allows to generate such corpora for many languages. There are initiatives to create and sustain spoken Wikipedia versions in many languages and hence the data is freely available, grows over time, and can be used for automatic corpus creation. Our pipeline automatically downloads and aligns this data. The resulting German corpus currently totals 293h of audio, of which we align 71h in full sentences and another 86h of sentences with some missing words. The English corpus consists of 287h, for which we align 27h in full sentence and 157h with some missing words. Results are publically available.
A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation
Authors: Robert Herms, Laura Seelig, Stefanie M{\"u}nch, Maximilian Eibl
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this Paper we present a corpus named SXUCorpus which contains read and spontaneous speech of the Upper Saxon German dialect. The data has been collected from eight archives of local television stations located in the Free State of Saxony. The recordings include broadcasted topics of news, economy, weather, sport, and documentation from the years 1992 to 1996 and have been manually transcribed and labeled. In the paper, we report the methodology of collecting and processing analog audiovisual material, constructing the corpus and describe the properties of the data. In its current version, the corpus is available to the scientific community and is designed for automatic speech recognition (ASR) evaluation with a development set and a test set. We performed ASR experiments with the open-source framework sphinx-4 including a configuration for Standard German on the dataset. Additionally, we show the influence of acoustic model and language model adaptation by the utilization of the development set.
Parallel Speech Corpora of Japanese Dialects
Authors: Koichiro Yoshino, Naoki Hirayama, Shinsuke Mori, Fumihiko Takahashi, Katsutoshi Itoyama, Hiroshi G. Okuno
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

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The TYPALOC Corpus: A Collection of Various Dysarthric Speech Recordings in Read and Spontaneous Styles
Authors: Christine Meunier, Cecile Fougeron, Corinne Fredouille, Brigitte Bigi, Lise Crevier-Buchman, Elisabeth Delais-Roussarie, Laurianne Georgeton, Alain Ghio, Imed Laaridh, Thierry Legou, Claire Pillot-Loiseau, Gilles Pouchoulin
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents the TYPALOC corpus of French Dysarthric and Healthy speech and the rationale underlying its constitution. The objective is to compare phonetic variation in the speech of dysarthric vs. healthy speakers in different speech conditions (read and unprepared speech). More precisely, we aim to compare the extent, types and location of phonetic variation within these different populations and speech conditions. The TYPALOC corpus is constituted of a selection of 28 dysarthric patients (three different pathologies) and of 12 healthy control speakers recorded while reading the same text and in a more natural continuous speech condition. Each audio signal has been segmented into Inter-Pausal Units. Then, the corpus has been manually transcribed and automatically aligned. The alignment has been corrected by an expert phonetician. Moreover, the corpus benefits from an automatic syllabification and an Automatic Detection of Acoustic Phone-Based Anomalies. Finally, in order to interpret phonetic variations due to pathologies, a perceptual evaluation of each patient has been conducted. Quantitative data are provided at the end of the paper.
A Longitudinal Bilingual Frisian-Dutch Radio Broadcast Database Designed for Code-Switching Research
Authors: Emre Yilmaz, Maaike Andringa, Sigrid Kingma, Jelske Dijkstra, Frits van der Kuip, Hans Van de Velde, Frederik Kampstra, Jouke Algra, Henk van den Heuvel, David van Leeuwen
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present a new speech database containing 18.5 hours of annotated radio broadcasts in the Frisian language. Frisian is mostly spoken in the province Fryslan and it is the second official language of the Netherlands. The recordings are collected from the archives of Omrop Fryslan, the regional public broadcaster of the province Fryslan. The database covers almost a 50-year time span. The native speakers of Frisian are mostly bilingual and often code-switch in daily conversations due to the extensive influence of the Dutch language. Considering the longitudinal and code-switching nature of the data, an appropriate annotation protocol has been designed and the data is manually annotated with the orthographic transcription, speaker identities, dialect information, code-switching details and background noise/music information.
The SI TEDx-UM speech database: a new Slovenian Spoken Language Resource
Authors: Andrej {\v{Z}}gank, Mirjam Sepesy Mau{\v{c}}ec, Darinka Verdonik
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

This paper presents a new Slovenian spoken language resource built from TEDx Talks. The speech database contains 242 talks in total duration of 54 hours. The annotation and transcription of acquired spoken material was generated automatically, applying acoustic segmentation and automatic speech recognition. The development and evaluation subset was also manually transcribed using the guidelines specified for the Slovenian GOS corpus. The manual transcriptions were used to evaluate the quality of unsupervised transcriptions. The average word error rate for the SI TEDx-UM evaluation subset was 50.7{\%}, with out of vocabulary rate of 24{\%} and language model perplexity of 390. The unsupervised transcriptions contain 372k tokens, where 32k of them were different.
Speech Corpus Spoken by Young-old, Old-old and Oldest-old Japanese
Authors: Yurie Iribe, Norihide Kitaoka, Shuhei Segawa
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: speech-recognition, Speech Recognition

We have constructed a new speech data corpus, using the utterances of 100 elderly Japanese people, to improve speech recognition accuracy of the speech of older people. Humanoid robots are being developed for use in elder care nursing homes. Interaction with such robots is expected to help maintain the cognitive abilities of nursing home residents, as well as providing them with companionship. In order for these robots to interact with elderly people through spoken dialogue, a high performance speech recognition system for speech of elderly people is needed. To develop such a system, we recorded speech uttered by 100 elderly Japanese, most of them are living in nursing homes, with an average age of 77.2. Previously, a seniors{'} speech corpus named S-JNAS was developed, but the average age of the participants was 67.6 years, but the target age for nursing home care is around 75 years old, much higher than that of the S-JNAS samples. In this paper we compare our new corpus with an existing Japanese read speech corpus, JNAS, which consists of adult speech, and with the above mentioned S-JNAS, the senior version of JNAS.
Polish Rhythmic Database ― New Resources for Speech Timing and Rhythm Analysis
Authors: Agnieszka Wagner, Katarzyna Klessa, Jolanta Bachan
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Management, Rhythm

This paper reports on a new database ― Polish rhythmic database and tools developed with the aim of investigating timing phenomena and rhythmic structure in Polish including topics such as, inter alia, the effect of speaking style and tempo on timing patterns, phonotactic and phrasal properties of speech rhythm and stability of rhythm metrics. So far, 19 native and 12 non-native speakers with different first languages have been recorded. The collected speech data (5 h 14 min.) represents five different speaking styles and five different tempi. For the needs of speech corpus management, annotation and analysis, a database was developed and integrated with Annotation Pro (Klessa et al., 2013, Klessa, 2016). Currently, the database is the only resource for Polish which allows for a systematic study of a broad range of phenomena related to speech timing and rhythm. The paper also introduces new tools and methods developed to facilitate the database annotation and analysis with respect to various timing and rhythm measures. In the end, the results of an ongoing research and first experimental results using the new resources are reported and future work is sketched.
An Extension of the Slovak Broadcast News Corpus based on Semi-Automatic Annotation
Authors: Peter Viszlay, J{\'a}n Sta{\v{s}}, Tom{\'a}{\v{s}} Koct{\'u}r, Martin Lojka, Jozef Juh{\'a}r
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: speech-recognition, Speech Recognition

In this paper, we introduce an extension of our previously released TUKE-BNews-SK corpus based on a semi-automatic annotation scheme. It firstly relies on the automatic transcription of the BN data performed by our Slovak large vocabulary continuous speech recognition system. The generated hypotheses are then manually corrected and completed by trained human annotators. The corpus is composed of 25 hours of fully-annotated spontaneous and prepared speech. In addition, we have acquired 900 hours of another BN data, part of which we plan to annotate semi-automatically. We present a preliminary corpus evaluation that gives very promising results.
Generating a Yiddish Speech Corpus, Forced Aligner and Basic ASR System for the AHEYM Project
Authors: Malgorzata {\'C}avar, Damir {\'C}avar, Dov-Ber Kerler, Anya Quilitzsch
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

To create automatic transcription and annotation tools for the AHEYM corpus of recorded interviews with Yiddish speakers in Eastern Europe we develop initial Yiddish language resources that are used for adaptations of speech and language technologies. Our project aims at the development of resources and technologies that can make the entire AHEYM corpus and other Yiddish resources more accessible to not only the community of Yiddish speakers or linguists with language expertise, but also historians and experts from other disciplines or the general public. In this paper we describe the rationale behind our approach, the procedures and methods, and challenges that are not specific to the AHEYM corpus, but apply to all documentary language data that is collected in the field. To the best of our knowledge, this is the first attempt to create a speech corpus and speech technologies for Yiddish. This is also the first attempt to work out speech and language technologies to transcribe and translate a large collection of Yiddish spoken language resources.
Coh-Metrix-Esp: A Complexity Analysis Tool for Documents Written in Spanish
Authors: Andre Quispesaravia, Walter Perez, Marco Sobrevilla Cabezudo, Fern Alva-Manchego, o
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Text Complexity Analysis is an useful task in Education. For example, it can help teachers select appropriate texts for their students according to their educational level. This task requires the analysis of several text features that people do mostly manually (e.g. syntactic complexity, words variety, etc.). In this paper, we present a tool useful for Complexity Analysis, called Coh-Metrix-Esp. This is the Spanish version of Coh-Metrix and is able to calculate 45 readability indices. We analyse how these indices behave in a corpus of {``}simple{''} and {``}complex{''} documents, and also use them as features in a complexity binary classifier for texts in Spanish. After some experiments with machine learning algorithms, we got 0.9 F-measure for a corpus that contains tales for kids and adults and 0.82 F-measure for a corpus with texts written for students of Spanish as a foreign language.
A Robust Panel Extraction Method for Manga
Authors: Xufang Pang, Ying Cao, Rynson W. H. Lau, and Antoni B. Chan
Date: 2014-01-01 00:00:00
Proceeding: Video, Image, and Sound Analysis Lab (VISAL) at the City University of Hong Kong! 2014 1
Tasks: Layout Design

Automatically extracting frames/panels from digital comic pages is crucial for techniques that facilitate comic reading on mobile devices with limited display areas. However, automatic panel extraction for manga, i.e., Japanese comics, can be especially challenging, largely because of its complex panel layout design mixed with various visual symbols throughout the page. In this paper, we propose a robust method for automatically extracting panels from digital manga pages. Our method first extracts the panel block by closing open panels and identifying a page background mask. It then performs a recursive binary splitting to partition the panel block into a set of sub-blocks, where an optimal splitting line at each recursive level is determined adaptively. Finally, it recovers accurate panel shapes from the computed subblocks. Our experiments show that the proposed method can robustly segment panels on the manga pages with various styles.
Transparency in Maintenance of Recruitment Chatbots
Authors: Kit Kuksenok, Nina Praß
Date: 2019-05-09 00:00:00
Tasks: BIG-bench Machine Learning

We report on experiences with implementing conversational agents in the recruitment domain based on a machine learning (ML) system. Recruitment chatbots mediate communication between job-seekers and recruiters by exposing ML data to recruiter teams. Errors are difficult to understand, communicate, and resolve because they may span and combine UX, ML, and software issues. In an effort to improve organizational and technical transparency, we came to rely on a key contact role. Though effective for design and development, the centralization of this role poses challenges for transparency in sustained maintenance of this kind of ML-based mediating system.
Multi-scale self-guided attention for medical image segmentation
Authors: Ashish Sinha, Jose Dolz
Date: 2019-06-07 00:00:00
Proceeding: arXiv preprint 2019 6
Tasks: Attentive segmentation networks, Brain Tumor Segmentation, Deep Attention, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of information, where similar low-level features are extracted multiple times at multiple scales. Second, long-range feature dependencies are not efficiently modeled, resulting in non-optimal discriminative feature representations associated with each semantic class. In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms. This approach is able to integrate local features with their corresponding global dependencies, as well as highlight interdependent channel maps in an adaptive manner. Further, the additional loss between different modules guides the attention mechanisms to neglect irrelevant information and focus on more discriminant regions of the image by emphasizing relevant feature associations. We evaluate the proposed model in the context of semantic segmentation on three different datasets: abdominal organs, cardiovascular structures and brain tumors. A series of ablation experiments support the importance of these attention modules in the proposed architecture. In addition, compared to other state-of-the-art segmentation networks our model yields better segmentation performance, increasing the accuracy of the predictions while reducing the standard deviation. This demonstrates the efficiency of our approach to generate precise and reliable automatic segmentations of medical images. Our code is made publicly available at https://github.com/sinAshish/Multi-Scale-Attention
Selfie: Self-supervised Pretraining for Image Embedding
Authors: Trieu H. Trinh, Minh-Thang Luong, Quoc V. Le
Date: 2019-06-07 00:00:00
Tasks: Language Modeling, Language Modelling, Masked Language Modeling

We introduce a pretraining technique called Selfie, which stands for SELFie supervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to continuous data, such as images, by making use of the Contrastive Predictive Coding loss (Oord et al., 2018). Given masked-out patches in an input image, our method learns to select the correct patch, among other "distractor" patches sampled from the same image, to fill in the masked location. This classification objective sidesteps the need for predicting exact pixel values of the target patches. The pretraining architecture of Selfie includes a network of convolutional blocks to process patches followed by an attention pooling network to summarize the content of unmasked patches before predicting masked ones. During finetuning, we reuse the convolutional weights found by pretraining. We evaluate Selfie on three benchmarks (CIFAR-10, ImageNet 32 x 32, and ImageNet 224 x 224) with varying amounts of labeled data, from 5% to 100% of the training sets. Our pretraining method provides consistent improvements to ResNet-50 across all settings compared to the standard supervised training of the same network. Notably, on ImageNet 224 x 224 with 60 examples per class (5%), our method improves the mean accuracy of ResNet-50 from 35.6% to 46.7%, an improvement of 11.1 points in absolute accuracy. Our pretraining method also improves ResNet-50 training stability, especially on low data regime, by significantly lowering the standard deviation of test accuracies across different runs.