Paper Group NANR 138
A Low Power, High Throughput, Fully Event-Based Stereo System. Active DOP: an Active Learning Constituency Treebank Annotation Tool. Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models. Constructing a Lexicon of English Discourse Connectives. Unsupervised Source Hierarchies for Low-Resource Neural Machine Transla …
A Low Power, High Throughput, Fully Event-Based Stereo System
| Title | A Low Power, High Throughput, Fully Event-Based Stereo System |
| Authors | Alexander Andreopoulos, Hirak J. Kashyap, Tapan K. Nayak, Arnon Amir, Myron D. Flickner |
| Abstract | We introduce a stereo correspondence system implemented fully on event-based digital hardware, using a fully graph-based non von-Neumann computation model, where no frames, arrays, or any other such data-structures are used. This is the first time that an end-to-end stereo pipeline from image acquisition and rectification, multi-scale spatio-temporal stereo correspondence, winner-take-all, to disparity regularization is implemented fully on event-based hardware. Using a cluster of TrueNorth neurosynaptic processors, we demonstrate their ability to process bilateral event-based inputs streamed live by Dynamic Vision Sensors (DVS), at up to 2,000 disparity maps per second, producing high fidelity disparities which are in turn used to reconstruct, at low power, the depth of events produced from rapidly changing scenes. Experiments on real-world sequences demonstrate the ability of the system to take full advantage of the asynchronous and sparse nature of DVS sensors for low power depth reconstruction, in environments where conventional frame-based cameras connected to synchronous processors would be inefficient for rapidly moving objects. System evaluation on event-based sequences demonstrates a ~200X improvement in terms of power per pixel per disparity map compared to the closest state-of-the-art, and maximum latencies of up to 11ms from spike injection to disparity map ejection. |
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| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Andreopoulos_A_Low_Power_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Andreopoulos_A_Low_Power_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/a-low-power-high-throughput-fully-event-based |
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Active DOP: an Active Learning Constituency Treebank Annotation Tool
| Title | Active DOP: an Active Learning Constituency Treebank Annotation Tool |
| Authors | Andreas van Cranenburgh |
| Abstract | We present a language-independent treebank annotation tool supporting rich annotations with discontinuous constituents and function tags. Candidate analyses are generated by an exemplar-based parsing model that immediately learns from each new annotated sentence during annotation. This makes it suitable for situations in which only a limited seed treebank is available, or a radically different domain is being annotated. The tool offers the possibility to experiment with and evaluate active learning methods to speed up annotation in a naturalistic setting, i.e., measuring actual annotation costs and tracking specific user interactions. The code is made available under the GNU GPL license at https://github.com/andreasvc/activedop. |
| Tasks | Active Learning, Feature Engineering |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-2009/ |
| https://www.aclweb.org/anthology/C18-2009 | |
| PWC | https://paperswithcode.com/paper/active-dop-an-active-learning-constituency |
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Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models
| Title | Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models |
| Authors | Lingzhen Chen, Aless Moschitti, ro |
| Abstract | In this paper, we propose to use a sequence to sequence model for Named Entity Recognition (NER) and we explore the effectiveness of such model in a progressive NER setting {–} a Transfer Learning (TL) setting. We train an initial model on source data and transfer it to a model that can recognize new NE categories in the target data during a subsequent step, when the source data is no longer available. Our solution consists in: (i) to reshape and re-parametrize the output layer of the first learned model to enable the recognition of new NEs; (ii) to leave the rest of the architecture unchanged, such that it is initialized with parameters transferred from the initial model; and (iii) to fine tune the network on the target data. Most importantly, we design a new NER approach based on sequence to sequence (Seq2Seq) models, which can intuitively work better in our progressive setting. We compare our approach with a Bidirectional LSTM, which is a strong neural NER model. Our experiments show that the Seq2Seq model performs very well on the standard NER setting and it is more robust in the progressive setting. Our approach can recognize previously unseen NE categories while preserving the knowledge of the seen data. |
| Tasks | Feature Engineering, Named Entity Recognition, Transfer Learning |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1185/ |
| https://www.aclweb.org/anthology/C18-1185 | |
| PWC | https://paperswithcode.com/paper/learning-to-progressively-recognize-new-named |
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Constructing a Lexicon of English Discourse Connectives
| Title | Constructing a Lexicon of English Discourse Connectives |
| Authors | Debopam Das, Tatjana Scheffler, Peter Bourgonje, Manfred Stede |
| Abstract | We present a new lexicon of English discourse connectives called DiMLex-Eng, built by merging information from two annotated corpora and an additional list of relation signals from the literature. The format follows the German connective lexicon DiMLex, which provides a cross-linguistically applicable XML schema. DiMLex-Eng contains 149 English connectives, and gives information on syntactic categories, discourse semantics and non-connective uses (if any). We report on the development steps and discuss design decisions encountered in the lexicon expansion phase. The resource is freely available for use in studies of discourse structure and computational applications. |
| Tasks | Machine Translation, Text Summarization |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-5042/ |
| https://www.aclweb.org/anthology/W18-5042 | |
| PWC | https://paperswithcode.com/paper/constructing-a-lexicon-of-english-discourse |
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Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation
| Title | Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation |
| Authors | Anna Currey, Kenneth Heafield |
| Abstract | Incorporating source syntactic information into neural machine translation (NMT) has recently proven successful (Eriguchi et al., 2016; Luong et al., 2016). However, this is generally done using an outside parser to syntactically annotate the training data, making this technique difficult to use for languages or domains for which a reliable parser is not available. In this paper, we introduce an unsupervised tree-to-sequence (tree2seq) model for neural machine translation; this model is able to induce an unsupervised hierarchical structure on the source sentence based on the downstream task of neural machine translation. We adapt the Gumbel tree-LSTM of Choi et al. (2018) to NMT in order to create the encoder. We evaluate our model against sequential and supervised parsing baselines on three low- and medium-resource language pairs. For low-resource cases, the unsupervised tree2seq encoder significantly outperforms the baselines; no improvements are seen for medium-resource translation. |
| Tasks | Low-Resource Neural Machine Translation, Machine Translation, Natural Language Inference, Sentiment Analysis |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-2902/ |
| https://www.aclweb.org/anthology/W18-2902 | |
| PWC | https://paperswithcode.com/paper/unsupervised-source-hierarchies-for-low |
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A Cross-lingual Messenger with Keyword Searchable Phrases for the Travel Domain
| Title | A Cross-lingual Messenger with Keyword Searchable Phrases for the Travel Domain |
| Authors | Shehroze Khan, Jihyun Kim, Tarik Zulfikarpasic, Peter Chen, Nizar Habash |
| Abstract | We present Qutr (Query Translator), a smart cross-lingual communication application for the travel domain. Qutr is a real-time messaging app that automatically translates conversations while supporting keyword-to-sentence matching. Qutr relies on querying a database that holds commonly used pre-translated travel-domain phrases and phrase templates in different languages with the use of keywords. The query matching supports paraphrases, incomplete keywords and some input spelling errors. The application addresses common cross-lingual communication issues such as translation accuracy, speed, privacy, and personalization. |
| Tasks | Machine Translation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-2033/ |
| https://www.aclweb.org/anthology/C18-2033 | |
| PWC | https://paperswithcode.com/paper/a-cross-lingual-messenger-with-keyword |
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Towards Pose Invariant Face Recognition in the Wild
| Title | Towards Pose Invariant Face Recognition in the Wild |
| Authors | Jian Zhao, Yu Cheng, Yan Xu, Lin Xiong, Jianshu Li, Fang Zhao, Karlekar Jayashree, Sugiri Pranata, Shengmei Shen, Junliang Xing, Shuicheng Yan, Jiashi Feng |
| Abstract | Pose variation is one key challenge in face recognition. As opposed to current techniques for pose invariant face recognition, which either directly extract pose invariant features for recognition, or first normalize profile face images to frontal pose before feature extraction, we argue that it is more desirable to perform both tasks jointly to allow them to benefit from each other. To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties. First, PIM is a novel and unified deep architecture, containing a Face Frontalization sub-Net (FFN) and a Discriminative Learning sub-Net (DLN), which are jointly learned from end to end. Second, FFN is a well-designed dual-path Generative Adversarial Network (GAN) which simultaneously perceives global structures and local details, incorporated with an unsupervised cross-domain adversarial training and a “learning to learn” strategy for high-fidelity and identity-preserving frontal view synthesis. Third, DLN is a generic Convolutional Neural Network (CNN) for face recognition with our enforced cross-entropy optimization strategy for learning discriminative yet generalized feature representation. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of the proposed model over the state-of-the-arts. |
| Tasks | Face Recognition, Robust Face Recognition |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhao_Towards_Pose_Invariant_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhao_Towards_Pose_Invariant_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/towards-pose-invariant-face-recognition-in |
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Subcharacter Information in Japanese Embeddings: When Is It Worth It?
| Title | Subcharacter Information in Japanese Embeddings: When Is It Worth It? |
| Authors | Marzena Karpinska, Bofang Li, Anna Rogers, Aleks Drozd, r |
| Abstract | Languages with logographic writing systems present a difficulty for traditional character-level models. Leveraging the subcharacter information was recently shown to be beneficial for a number of intrinsic and extrinsic tasks in Chinese. We examine whether the same strategies could be applied for Japanese, and contribute a new analogy dataset for this language. |
| Tasks | Text Classification, Tokenization |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-2905/ |
| https://www.aclweb.org/anthology/W18-2905 | |
| PWC | https://paperswithcode.com/paper/subcharacter-information-in-japanese |
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Enhanced Aspect Level Sentiment Classification with Auxiliary Memory
| Title | Enhanced Aspect Level Sentiment Classification with Auxiliary Memory |
| Authors | Peisong Zhu, Tieyun Qian |
| Abstract | In aspect level sentiment classification, there are two common tasks: to identify the sentiment of an aspect (category) or a term. As specific instances of aspects, terms explicitly occur in sentences. It is beneficial for models to focus on nearby context words. In contrast, as high level semantic concepts of terms, aspects usually have more generalizable representations. However, conventional methods cannot utilize the information of aspects and terms at the same time, because few datasets are annotated with both aspects and terms. In this paper, we propose a novel deep memory network with auxiliary memory to address this problem. In our model, a main memory is used to capture the important context words for sentiment classification. In addition, we build an auxiliary memory to implicitly convert aspects and terms to each other, and feed both of them to the main memory. With the interaction between two memories, the features of aspects and terms can be learnt simultaneously. We compare our model with the state-of-the-art methods on four datasets from different domains. The experimental results demonstrate the effectiveness of our model. |
| Tasks | Feature Engineering, Machine Translation, Question Answering, Sentiment Analysis, Text Classification, Text Summarization |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1092/ |
| https://www.aclweb.org/anthology/C18-1092 | |
| PWC | https://paperswithcode.com/paper/enhanced-aspect-level-sentiment |
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The price of debiasing automatic metrics in natural language evalaution
| Title | The price of debiasing automatic metrics in natural language evalaution |
| Authors | Arun Chaganty, Stephen Mussmann, Percy Liang |
| Abstract | For evaluating generation systems, automatic metrics such as BLEU cost nothing to run but have been shown to correlate poorly with human judgment, leading to systematic bias against certain model improvements. On the other hand, averaging human judgments, the unbiased gold standard, is often too expensive. In this paper, we use control variates to combine automatic metrics with human evaluation to obtain an unbiased estimator with lower cost than human evaluation alone. In practice, however, we obtain only a 7-13{%} cost reduction on evaluating summarization and open-response question answering systems. We then prove that our estimator is optimal: there is no unbiased estimator with lower cost. Our theory further highlights the two fundamental bottlenecks{—}the automatic metric and the prompt shown to human evaluators{—}both of which need to be improved to obtain greater cost savings. |
| Tasks | Abstractive Text Summarization, Image Captioning, Question Answering |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1060/ |
| https://www.aclweb.org/anthology/P18-1060 | |
| PWC | https://paperswithcode.com/paper/the-price-of-debiasing-automatic-metrics-in-1 |
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Open Information Extraction from Conjunctive Sentences
| Title | Open Information Extraction from Conjunctive Sentences |
| Authors | Swarnadeep Saha, {Mausam} |
| Abstract | We develop CALM, a coordination analyzer that improves upon the conjuncts identified from dependency parses. It uses a language model based scoring and several linguistic constraints to search over hierarchical conjunct boundaries (for nested coordination). By splitting a conjunctive sentence around these conjuncts, CALM outputs several simple sentences. We demonstrate the value of our coordination analyzer in the end task of Open Information Extraction (Open IE). State-of-the-art Open IE systems lose substantial yield due to ineffective processing of conjunctive sentences. Our Open IE system, CALMIE, performs extraction over the simple sentences identified by CALM to obtain up to 1.8x yield with a moderate increase in precision compared to extractions from original sentences. |
| Tasks | Language Modelling, Open Information Extraction |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1194/ |
| https://www.aclweb.org/anthology/C18-1194 | |
| PWC | https://paperswithcode.com/paper/open-information-extraction-from-conjunctive |
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Text Normalization Infrastructure that Scales to Hundreds of Language Varieties
| Title | Text Normalization Infrastructure that Scales to Hundreds of Language Varieties |
| Authors | Mason Chua, Daan van Esch, Noah Coccaro, Eunjoon Cho, Bh, Sujeet ari, Libin Jia |
| Abstract | |
| Tasks | Language Identification, Language Modelling, Speech Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1216/ |
| https://www.aclweb.org/anthology/L18-1216 | |
| PWC | https://paperswithcode.com/paper/text-normalization-infrastructure-that-scales |
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Text Completion using Context-Integrated Dependency Parsing
| Title | Text Completion using Context-Integrated Dependency Parsing |
| Authors | Amr Rekaby Salama, {"O}zge Ala{\c{c}}am, Wolfgang Menzel |
| Abstract | Incomplete linguistic input, i.e. due to a noisy environment, is one of the challenges that a successful communication system has to deal with. In this paper, we study text completion with a data set composed of sentences with gaps where a successful completion cannot be achieved through a uni-modal (language-based) approach. We present a solution based on a context-integrating dependency parser incorporating an additional non-linguistic modality. An incompleteness in one channel is compensated by information from another one and the parser learns the association between the two modalities from a multiple level knowledge representation. We examined several model variations by adjusting the degree of influence of different modalities in the decision making on possible filler words and their exact reference to a non-linguistic context element. Our model is able to fill the gap with 95.4{%} word and 95.2{%} exact reference accuracy hence the successful prediction can be achieved not only on the word level (such as mug) but also with respect to the correct identification of its context reference (such as mug 2 among several mug instances). |
| Tasks | Decision Making, Dependency Parsing, Language Modelling, Representation Learning |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-3005/ |
| https://www.aclweb.org/anthology/W18-3005 | |
| PWC | https://paperswithcode.com/paper/text-completion-using-context-integrated |
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Comparison of Representations of Named Entities for Document Classification
| Title | Comparison of Representations of Named Entities for Document Classification |
| Authors | Lidia Pivovarova, Roman Yangarber |
| Abstract | We explore representations for multi-word names in text classification tasks, on Reuters (RCV1) topic and sector classification. We find that: the best way to treat names is to split them into tokens and use each token as a separate feature; NEs have more impact on sector classification than topic classification; replacing NEs with entity types is not an effective strategy; representing tokens by different embeddings for proper names vs. common nouns does not improve results. We highlight the improvements over state-of-the-art results that our CNN models yield. |
| Tasks | Document Classification, Representation Learning, Text Classification, Word Embeddings |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-3008/ |
| https://www.aclweb.org/anthology/W18-3008 | |
| PWC | https://paperswithcode.com/paper/comparison-of-representations-of-named |
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Detecting Linguistic Traces of Depression in Topic-Restricted Text: Attending to Self-Stigmatized Depression with NLP
| Title | Detecting Linguistic Traces of Depression in Topic-Restricted Text: Attending to Self-Stigmatized Depression with NLP |
| Authors | JT Wolohan, Misato Hiraga, Atreyee Mukherjee, Zeeshan Ali Sayyed, Matthew Millard |
| Abstract | Natural language processing researchers have proven the ability of machine learning approaches to detect depression-related cues from language; however, to date, these efforts have primarily assumed it was acceptable to leave depression-related texts in the data. Our concerns with this are twofold: first, that the models may be overfitting on depression-related signals, which may not be present in all depressed users (only those who talk about depression on social media); and second, that these models would under-perform for users who are sensitive to the public stigma of depression. This study demonstrates the validity to those concerns. We construct a novel corpus of texts from 12,106 Reddit users and perform lexical and predictive analyses under two conditions: one where all text produced by the users is included and one where the depression data is withheld. We find significant differences in the language used by depressed users under the two conditions as well as a difference in the ability of machine learning algorithms to correctly detect depression. However, despite the lexical differences and reduced classification performance{–}each of which suggests that users may be able to fool algorithms by avoiding direct discussion of depression{–}a still respectable overall performance suggests lexical models are reasonably robust and well suited for a role in a diagnostic or monitoring capacity. |
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| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4102/ |
| https://www.aclweb.org/anthology/W18-4102 | |
| PWC | https://paperswithcode.com/paper/detecting-linguistic-traces-of-depression-in |
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