Paper Group NANR 53
Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction. Annotating Opinions and Opinion Targets in Student Course Feedback. Disentangled activations in deep networks. Towards an Automatic Classification of Illustrative Examples in a Large Japanese-French Dictionary Obtained by OCR. AMR Beyond the Sentence: the Multi-se …
Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction
| Title | Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction |
| Authors | Procheta Sen, Debasis Ganguly, Gareth Jones |
| Abstract | Task extraction is the process of identifying search intents over a set of queries potentially spanning multiple search sessions. Most existing research on task extraction has focused on identifying tasks within a single session, where the notion of a session is defined by a fixed length time window. By contrast, in this work we seek to identify tasks that span across multiple sessions. To identify tasks, we conduct a global analysis of a query log in its entirety without restricting analysis to individual temporal windows. To capture inherent task semantics, we represent queries as vectors in an abstract space. We learn the embedding of query words in this space by leveraging the temporal and lexical contexts of queries. Embedded query vectors are then clustered into tasks. Experiments demonstrate that task extraction effectiveness is improved significantly with our proposed method of query vector embedding in comparison to existing approaches that make use of documents retrieved from a collection to estimate semantic similarities between queries. |
| Tasks | Information Retrieval |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-1026/ |
| https://www.aclweb.org/anthology/N18-1026 | |
| PWC | https://paperswithcode.com/paper/tempo-lexical-context-driven-word-embedding |
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Annotating Opinions and Opinion Targets in Student Course Feedback
| Title | Annotating Opinions and Opinion Targets in Student Course Feedback |
| Authors | Janaka Chathuranga, Shanika Ediriweera, Ravindu Hasantha, Pranidhith Munasinghe, Surangika Ranathunga |
| Abstract | |
| Tasks | Sentiment Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1425/ |
| https://www.aclweb.org/anthology/L18-1425 | |
| PWC | https://paperswithcode.com/paper/annotating-opinions-and-opinion-targets-in |
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Disentangled activations in deep networks
| Title | Disentangled activations in deep networks |
| Authors | Mikael Kågebäck, Olof Mogren |
| Abstract | Deep neural networks have been tremendously successful in a number of tasks. One of the main reasons for this is their capability to automatically learn representations of data in levels of abstraction, increasingly disentangling the data as the internal transformations are applied. In this paper we propose a novel regularization method that penalize covariance between dimensions of the hidden layers in a network, something that benefits the disentanglement. This makes the network learn nonlinear representations that are linearly uncorrelated, yet allows the model to obtain good results on a number of tasks, as demonstrated by our experimental evaluation. The proposed technique can be used to find the dimensionality of the underlying data, because it effectively disables dimensions that aren’t needed. Our approach is simple and computationally cheap, as it can be applied as a regularizer to any gradient-based learning model. |
| Tasks | |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=ByzvHagA- |
| https://openreview.net/pdf?id=ByzvHagA- | |
| PWC | https://paperswithcode.com/paper/disentangled-activations-in-deep-networks |
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Towards an Automatic Classification of Illustrative Examples in a Large Japanese-French Dictionary Obtained by OCR
| Title | Towards an Automatic Classification of Illustrative Examples in a Large Japanese-French Dictionary Obtained by OCR |
| Authors | Christian Boitet, Mathieu Mangeot, Mutsuko Tomokiyo |
| Abstract | We work on improving the Cesselin, a large and open source Japanese-French bilingual dictionary digitalized by OCR, available on the web, and contributively improvable online. Labelling its examples (about 226000) would significantly enhance their usefulness for language learners. Examples are proverbs, idiomatic constructions, normal usage examples, and, for nouns, phrases containing a quantifier. Proverbs are easy to spot, but not examples of other types. To find a method for automatically or at least semi-automatically annotating them, we have studied many entries, and hypothesized that the degree of lexical similarity between results of MT into a third language might give good cues. To confirm that hypothesis, we sampled 500 examples and used Google Translate to translate into English their Japanese expressions and their French translations. The hypothesis holds well, in particular for distinguishing examples of normal usage from idiomatic examples. Finally, we propose a detailed annotation procedure and discuss its future automatization. |
| Tasks | Machine Translation, Optical Character Recognition |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-3815/ |
| https://www.aclweb.org/anthology/W18-3815 | |
| PWC | https://paperswithcode.com/paper/towards-an-automatic-classification-of |
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AMR Beyond the Sentence: the Multi-sentence AMR corpus
| Title | AMR Beyond the Sentence: the Multi-sentence AMR corpus |
| Authors | Tim O{'}Gorman, Michael Regan, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Martha Palmer |
| Abstract | There are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference and similar phenomena (bridging and implicit roles) on top of gold Abstract Meaning Representations of sentence-level semantics. We present a new corpus of this annotation, with analysis of its quality, alongside a plausible baseline for comparison. It is hoped that this Multi-Sentence AMR corpus (MS-AMR) may become a feasible method for developing rich representations of document meaning, useful for tasks such as information extraction and question answering. |
| Tasks | Question Answering |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1313/ |
| https://www.aclweb.org/anthology/C18-1313 | |
| PWC | https://paperswithcode.com/paper/amr-beyond-the-sentence-the-multi-sentence |
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An automated medical scribe for documenting clinical encounters
| Title | An automated medical scribe for documenting clinical encounters |
| Authors | Gregory Finley, Erik Edwards, Am Robinson, a, Michael Brenndoerfer, Najmeh Sadoughi, James Fone, Nico Axtmann, Mark Miller, David Suendermann-Oeft |
| Abstract | A medical scribe is a clinical professional who charts patient{–}physician encounters in real time, relieving physicians of most of their administrative burden and substantially increasing productivity and job satisfaction. We present a complete implementation of an automated medical scribe. Our system can serve either as a scalable, standardized, and economical alternative to human scribes; or as an assistive tool for them, providing a first draft of a report along with a convenient means to modify it. This solution is, to our knowledge, the first automated scribe ever presented and relies upon multiple speech and language technologies, including speaker diarization, medical speech recognition, knowledge extraction, and natural language generation. |
| Tasks | Speaker Diarization, Speech Recognition, Text Generation |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-5003/ |
| https://www.aclweb.org/anthology/N18-5003 | |
| PWC | https://paperswithcode.com/paper/an-automated-medical-scribe-for-documenting |
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Variational Autoregressive Decoder for Neural Response Generation
| Title | Variational Autoregressive Decoder for Neural Response Generation |
| Authors | Jiachen Du, Wenjie Li, Yulan He, Ruifeng Xu, Lidong Bing, Xuan Wang |
| Abstract | Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation. However, existing CVAE-based models often generate responses from a single latent variable which may not be sufficient to model high variability in responses. To solve this problem, we propose a novel model that sequentially introduces a series of latent variables to condition the generation of each word in the response sequence. In addition, the approximate posteriors of these latent variables are augmented with a backward Recurrent Neural Network (RNN), which allows the latent variables to capture long-term dependencies of future tokens in generation. To facilitate training, we supplement our model with an auxiliary objective that predicts the subsequent bag of words. Empirical experiments conducted on Opensubtitle and Reddit datasets show that the proposed model leads to significant improvement on both relevance and diversity over state-of-the-art baselines. |
| Tasks | |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1354/ |
| https://www.aclweb.org/anthology/D18-1354 | |
| PWC | https://paperswithcode.com/paper/variational-autoregressive-decoder-for-neural |
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Multiple-gaze geometry: Inferring novel 3D locations from gazes observed in monocular video
| Title | Multiple-gaze geometry: Inferring novel 3D locations from gazes observed in monocular video |
| Authors | Ernesto Brau, Jinyan Guan, Tanya Jeffries, Kobus Barnard |
| Abstract | We develop using person gaze direction for scene understanding. In particular, we use intersecting gazes to learn 3D locations that people tend to look at, which is analogous to having multiple camera views. The 3D locations that we discover need not be visible to the camera. Conversely, knowing 3D locations of scene elements that draw visual attention, such as other people in the scene, can help infer gaze direction. We provide a Bayesian generative model for the temporal scene that captures the joint probability of camera parameters, locations of people, their gaze, what they are looking at, and locations of visual attention. Both the number of people in the scene and the number of extra objects that draw attention are unknown and need to be inferred. To execute this joint inference we use a probabilistic data association approach that enables principled comparison of model hypotheses. We use MCMC for inference over the discrete correspondence variables, and approximate the marginalization over continuous parameters using the Metropolis-Laplace approximation, using Hamiltonian (Hybrid) Monte Carlo for maximization. As existing data sets do not provide the 3D locations of what people are looking at, we contribute a small data set that does. On this data set, we infer what people are looking at with 59% precision compared with 13% for a baseline approach, and where those objects are within about 0.58m. |
| Tasks | Scene Understanding |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Ernesto_Brau_Stereo_gaze_Inferring_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Ernesto_Brau_Stereo_gaze_Inferring_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/multiple-gaze-geometry-inferring-novel-3d |
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Exploiting Pre-Ordering for Neural Machine Translation
| Title | Exploiting Pre-Ordering for Neural Machine Translation |
| Authors | Yang Zhao, Jiajun Zhang, Chengqing Zong |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1143/ |
| https://www.aclweb.org/anthology/L18-1143 | |
| PWC | https://paperswithcode.com/paper/exploiting-pre-ordering-for-neural-machine |
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SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages
| Title | SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages |
| Authors | Siamak Barzegar, Brian Davis, Manel Zarrouk, H, Siegfried schuh, Andre Freitas |
| Abstract | |
| Tasks | Information Retrieval, Machine Translation, Question Answering, Semantic Similarity, Semantic Textual Similarity, Word Embeddings |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1618/ |
| https://www.aclweb.org/anthology/L18-1618 | |
| PWC | https://paperswithcode.com/paper/semr-11-a-multi-lingual-gold-standard-for |
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Robust Word Vectors: Context-Informed Embeddings for Noisy Texts
| Title | Robust Word Vectors: Context-Informed Embeddings for Noisy Texts |
| Authors | Valentin Malykh, Varvara Logacheva, Taras Khakhulin |
| Abstract | We suggest a new language-independent architecture of robust word vectors (RoVe). It is designed to alleviate the issue of typos, which are common in almost any user-generated content, and hinder automatic text processing. Our model is morphologically motivated, which allows it to deal with unseen word forms in morphologically rich languages. We present the results on a number of Natural Language Processing (NLP) tasks and languages for the variety of related architectures and show that proposed architecture is typo-proof. |
| Tasks | Morphological Analysis, Word Embeddings |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-6108/ |
| https://www.aclweb.org/anthology/W18-6108 | |
| PWC | https://paperswithcode.com/paper/robust-word-vectors-context-informed |
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The ICoN Corpus of Academic Written Italian (L1 and L2)
| Title | The ICoN Corpus of Academic Written Italian (L1 and L2) |
| Authors | Mirko Tavosanis, Federica Cominetti |
| Abstract | |
| Tasks | |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1643/ |
| https://www.aclweb.org/anthology/L18-1643 | |
| PWC | https://paperswithcode.com/paper/the-icon-corpus-of-academic-written-italian |
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Sarcasm Target Identification: Dataset and An Introductory Approach
| Title | Sarcasm Target Identification: Dataset and An Introductory Approach |
| Authors | Aditya Joshi, Pranav Goel, Pushpak Bhattacharyya, Mark Carman |
| Abstract | |
| Tasks | Sarcasm Detection, Sentiment Analysis, Text Generation, Topic Models |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1424/ |
| https://www.aclweb.org/anthology/L18-1424 | |
| PWC | https://paperswithcode.com/paper/sarcasm-target-identification-dataset-and-an |
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Spectral Graph Wavelets for Structural Role Similarity in Networks
| Title | Spectral Graph Wavelets for Structural Role Similarity in Networks |
| Authors | Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec |
| Abstract | Nodes residing in different parts of a graph can have similar structural roles within their local network topology. The identification of such roles provides key insight into the organization of networks and can also be used to inform machine learning on graphs. However, learning structural representations of nodes is a challenging unsupervised-learning task, which typically involves manually specifying and tailoring topological features for each node. Here we develop GraphWave, a method that represents each node’s local network neighborhood via a low-dimensional embedding by leveraging spectral graph wavelet diffusion patterns. We prove that nodes with similar local network neighborhoods will have similar GraphWave embeddings even though these nodes may reside in very different parts of the network. Our method scales linearly with the number of edges and does not require any hand-tailoring of topological features. We evaluate performance on both synthetic and real-world datasets, obtaining improvements of up to 71% over state-of-the-art baselines. |
| Tasks | |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=rJR2ylbRb |
| https://openreview.net/pdf?id=rJR2ylbRb | |
| PWC | https://paperswithcode.com/paper/spectral-graph-wavelets-for-structural-role |
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Zero-shot Relation Classification as Textual Entailment
| Title | Zero-shot Relation Classification as Textual Entailment |
| Authors | Abiola Obamuyide, Andreas Vlachos |
| Abstract | We consider the task of relation classification, and pose this task as one of textual entailment. We show that this formulation leads to several advantages, including the ability to (i) perform zero-shot relation classification by exploiting relation descriptions, (ii) utilize existing textual entailment models, and (iii) leverage readily available textual entailment datasets, to enhance the performance of relation classification systems. Our experiments show that the proposed approach achieves 20.16{%} and 61.32{%} in F1 zero-shot classification performance on two datasets, which further improved to 22.80{%} and 64.78{%} respectively with the use of conditional encoding. |
| Tasks | Knowledge Base Population, Natural Language Inference, Question Answering, Relation Classification, Relation Extraction, Zero-Shot Learning |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5511/ |
| https://www.aclweb.org/anthology/W18-5511 | |
| PWC | https://paperswithcode.com/paper/zero-shot-relation-classification-as-textual |
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