Paper Group NANR 18
Using relative entropy for detection and analysis of periods of diachronic linguistic change. English-Basque Statistical and Neural Machine Translation. A corpus of German political speeches from the 21st century. Automatic Enrichment of Terminological Resources: the IATE RDF Example. A Comparative Study of Extremely Low-Resource Transliteration of …
Using relative entropy for detection and analysis of periods of diachronic linguistic change
| Title | Using relative entropy for detection and analysis of periods of diachronic linguistic change |
| Authors | Stefania Degaetano-Ortlieb, Elke Teich |
| Abstract | We present a data-driven approach to detect periods of linguistic change and the lexical and grammatical features contributing to change. We focus on the development of scientific English in the late modern period. Our approach is based on relative entropy (Kullback-Leibler Divergence) comparing temporally adjacent periods and sliding over the time line from past to present. Using a diachronic corpus of scientific publications of the Royal Society of London, we show how periods of change reflect the interplay between lexis and grammar, where periods of lexical expansion are typically followed by periods of grammatical consolidation resulting in a balance between expressivity and communicative efficiency. Our method is generic and can be applied to other data sets, languages and time ranges. |
| Tasks | |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4503/ |
| https://www.aclweb.org/anthology/W18-4503 | |
| PWC | https://paperswithcode.com/paper/using-relative-entropy-for-detection-and |
| Repo | |
| Framework | |
English-Basque Statistical and Neural Machine Translation
| Title | English-Basque Statistical and Neural Machine Translation |
| Authors | Inigo Jauregi Unanue, Lierni Garmendia Arratibel, Ehsan Zare Borzeshi, Massimo Piccardi |
| Abstract | |
| Tasks | Machine Translation, Named Entity Recognition, Natural Language Inference, Transfer Learning |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1141/ |
| https://www.aclweb.org/anthology/L18-1141 | |
| PWC | https://paperswithcode.com/paper/english-basque-statistical-and-neural-machine |
| Repo | |
| Framework | |
A corpus of German political speeches from the 21st century
| Title | A corpus of German political speeches from the 21st century |
| Authors | Adrien Barbaresi |
| Abstract | |
| Tasks | Keyword Extraction, Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1127/ |
| https://www.aclweb.org/anthology/L18-1127 | |
| PWC | https://paperswithcode.com/paper/a-corpus-of-german-political-speeches-from |
| Repo | |
| Framework | |
Automatic Enrichment of Terminological Resources: the IATE RDF Example
| Title | Automatic Enrichment of Terminological Resources: the IATE RDF Example |
| Authors | Mihael Arcan, Elena Montiel-Ponsoda, John P. McCrae, Paul Buitelaar |
| Abstract | |
| Tasks | Machine Translation, Word Sense Disambiguation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1149/ |
| https://www.aclweb.org/anthology/L18-1149 | |
| PWC | https://paperswithcode.com/paper/automatic-enrichment-of-terminological |
| Repo | |
| Framework | |
A Comparative Study of Extremely Low-Resource Transliteration of the World’s Languages
| Title | A Comparative Study of Extremely Low-Resource Transliteration of the World’s Languages |
| Authors | Winston Wu, David Yarowsky |
| Abstract | |
| Tasks | Machine Translation, Speech Recognition, Transliteration |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1150/ |
| https://www.aclweb.org/anthology/L18-1150 | |
| PWC | https://paperswithcode.com/paper/a-comparative-study-of-extremely-low-resource |
| Repo | |
| Framework | |
All-words Word Sense Disambiguation Using Concept Embeddings
| Title | All-words Word Sense Disambiguation Using Concept Embeddings |
| Authors | Rui Suzuki, Kanako Komiya, Masayuki Asahara, Minoru Sasaki, Hiroyuki Shinnou |
| Abstract | |
| Tasks | Word Embeddings, Word Sense Disambiguation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1162/ |
| https://www.aclweb.org/anthology/L18-1162 | |
| PWC | https://paperswithcode.com/paper/all-words-word-sense-disambiguation-using |
| Repo | |
| Framework | |
Persian Discourse Treebank and coreference corpus
| Title | Persian Discourse Treebank and coreference corpus |
| Authors | Azadeh Mirzaei, Pegah Safari |
| Abstract | |
| Tasks | Coreference Resolution, Semantic Role Labeling |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1638/ |
| https://www.aclweb.org/anthology/L18-1638 | |
| PWC | https://paperswithcode.com/paper/persian-discourse-treebank-and-coreference |
| Repo | |
| Framework | |
Spline Error Weighting for Robust Visual-Inertial Fusion
| Title | Spline Error Weighting for Robust Visual-Inertial Fusion |
| Authors | Hannes Ovrén, Per-Erik Forssén |
| Abstract | In this paper we derive and test a probability-based weighting that can balance residuals of different types in spline fitting. In contrast to previous formulations, the proposed spline error weighting scheme also incorporates a prediction of the approximation error of the spline fit. We demonstrate the effectiveness of the prediction in a synthetic experiment, and apply it to visual-inertial fusion on rolling shutter cameras. This results in a method that can estimate 3D structure with metric scale on generic first-person videos. We also propose a quality measure for spline fitting, that can be used to automatically select the knot spacing. Experiments verify that the obtained trajectory quality corresponds well with the requested quality. Finally, by linearly scaling the weights, we show that the proposed spline error weighting minimizes the estimation errors on real sequences, in terms of scale and end-point errors. |
| Tasks | |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Ovren_Spline_Error_Weighting_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Ovren_Spline_Error_Weighting_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/spline-error-weighting-for-robust-visual |
| Repo | |
| Framework | |
Parser combinators for Tigrinya and Oromo morphology
| Title | Parser combinators for Tigrinya and Oromo morphology |
| Authors | Patrick Littell, Tom McCoy, Na-Rae Han, Shruti Rijhwani, Zaid Sheikh, David Mortensen, Teruko Mitamura, Lori Levin |
| Abstract | |
| Tasks | Lemmatization, Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1611/ |
| https://www.aclweb.org/anthology/L18-1611 | |
| PWC | https://paperswithcode.com/paper/parser-combinators-for-tigrinya-and-oromo |
| Repo | |
| Framework | |
Using a Corpus of English and Chinese Political Speeches for Metaphor Analysis
| Title | Using a Corpus of English and Chinese Political Speeches for Metaphor Analysis |
| Authors | Kathleen Ahrens, Huiheng Zeng, Shun-han Rebekah Wong |
| Abstract | |
| Tasks | |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1160/ |
| https://www.aclweb.org/anthology/L18-1160 | |
| PWC | https://paperswithcode.com/paper/using-a-corpus-of-english-and-chinese |
| Repo | |
| Framework | |
Interpersonal Relationship Labels for the CALLHOME Corpus
| Title | Interpersonal Relationship Labels for the CALLHOME Corpus |
| Authors | Denys Katerenchuk, David Guy Brizan, Andrew Rosenberg |
| Abstract | |
| Tasks | Speech Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1592/ |
| https://www.aclweb.org/anthology/L18-1592 | |
| PWC | https://paperswithcode.com/paper/interpersonal-relationship-labels-for-the |
| Repo | |
| Framework | |
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection
| Title | Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection |
| Authors | Massimo Nicosia, Aless Moschitti, ro |
| Abstract | State-of-the-art networks that model relations between two pieces of text often use complex architectures and attention. In this paper, instead of focusing on architecture engineering, we take advantage of small amounts of labelled data that model semantic phenomena in text to encode matching features directly in the word representations. This greatly boosts the accuracy of our reference network, while keeping the model simple and fast to train. Our approach also beats a tree kernel model that uses similar input encodings, and neural models which use advanced attention and compare-aggregate mechanisms. |
| Tasks | Feature Engineering, Question Answering |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1133/ |
| https://www.aclweb.org/anthology/D18-1133 | |
| PWC | https://paperswithcode.com/paper/semantic-linking-in-convolutional-neural |
| Repo | |
| Framework | |
WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art
| Title | WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art |
| Authors | Saif Mohammad, Svetlana Kiritchenko |
| Abstract | |
| Tasks | Emotion Recognition, Image Generation, Image Retrieval |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1197/ |
| https://www.aclweb.org/anthology/L18-1197 | |
| PWC | https://paperswithcode.com/paper/wikiart-emotions-an-annotated-dataset-of |
| Repo | |
| Framework | |
Cross-Modal Hamming Hashing
| Title | Cross-Modal Hamming Hashing |
| Authors | Yue Cao , Bin Liu, Mingsheng Long, Jianmin Wang |
| Abstract | Cross-modal hashing enables similarity retrieval across different content modalities, such as searching relevant images in response to text queries. It provides with the advantages of computation efficiency and retrieval quality for multimedia retrieval. Hamming space retrieval enables efficient constant-time search that returns data items within a given Hamming radius to each query, by hash lookups instead of linear scan. However, Hamming space retrieval is ineffective in existing cross-modal hashing methods, subject to their weak capability of concentrating the relevant items to be within a small Hamming ball, while worse still, the Hamming distances between hash codes from different modalities are inevitably large due to the large heterogeneity across different modalities. This work presents Cross-Modal Hamming Hashing (CMHH), a novel deep cross-modal hashing approach that generates compact and highly concentrated hash codes to enable efficient and effective Hamming space retrieval. The main idea is to penalize significantly on similar cross-modal pairs with Hamming distance larger than the Hamming radius threshold, by designing a pairwise focal loss based on the exponential distribution. Extensive experiments demonstrate that CMHH can generate highly concentrated hash codes and achieve state-of-the-art cross-modal retrieval performance for both hash lookups and linear scan scenarios on three benchmark datasets, NUS-WIDE, MIRFlickr-25K, and IAPR TC-12. |
| Tasks | Cross-Modal Retrieval |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Yue_Cao_Cross-Modal_Hamming_Hashing_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Yue_Cao_Cross-Modal_Hamming_Hashing_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/cross-modal-hamming-hashing |
| Repo | |
| Framework | |
What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis
| Title | What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis |
| Authors | Philip John Gorinski, Mirella Lapata |
| Abstract | This work takes a first step toward movie content analysis by tackling the novel task of movie overview generation. Overviews are natural language texts that give a first impression of a movie, describing aspects such as its genre, plot, mood, or artistic style. We create a dataset that consists of movie scripts, attribute-value pairs for the movies{'} aspects, as well as overviews, which we extract from an online database. We present a novel end-to-end model for overview generation, consisting of a multi-label encoder for identifying screenplay attributes, and an LSTM decoder to generate natural language sentences conditioned on the identified attributes. Automatic and human evaluation show that the encoder is able to reliably assign good labels for the movie{'}s attributes, and the overviews provide descriptions of the movie{'}s content which are informative and faithful. |
| Tasks | Decision Making |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-1160/ |
| https://www.aclweb.org/anthology/N18-1160 | |
| PWC | https://paperswithcode.com/paper/whatas-this-movie-about-a-joint-neural |
| Repo | |
| Framework | |
