Paper Group NANR 88
Making ``fetch’’ happen: The influence of social and linguistic context on nonstandard word growth and decline. Neural sentence generation from formal semantics. A Model for Learned Bloom Filters and Optimizing by Sandwiching. Maximizing Induced Cardinality Under a Determinantal Point Process. Anaphora Resolution with the ARRAU Corpus. Supervising …
Making ``fetch’’ happen: The influence of social and linguistic context on nonstandard word growth and decline
| Title | Making ``fetch’’ happen: The influence of social and linguistic context on nonstandard word growth and decline | |
| Authors | Ian Stewart, Jacob Eisenstein |
| Abstract | In an online community, new words come and go: today{'}s {}haha{''} may be replaced by tomorrow{'}s {}lol.{''} Changes in online writing are usually studied as a social process, with innovations diffusing through a network of individuals in a speech community. But unlike other types of innovation, language change is shaped and constrained by the grammatical system in which it takes part. To investigate the role of social and structural factors in language change, we undertake a large-scale analysis of the frequencies of non-standard words in Reddit. Dissemination across many linguistic contexts is a predictor of success: words that appear in more linguistic contexts grow faster and survive longer. Furthermore, social dissemination plays a less important role in explaining word growth and decline than previously hypothesized. |
| Tasks | |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1467/ |
| https://www.aclweb.org/anthology/D18-1467 | |
| PWC | https://paperswithcode.com/paper/making-fetch-happen-the-influence-of-social-2 |
| Repo | |
| Framework | |
Neural sentence generation from formal semantics
| Title | Neural sentence generation from formal semantics |
| Authors | Kana Manome, Masashi Yoshikawa, Hitomi Yanaka, Pascual Mart{'\i}nez-G{'o}mez, Koji Mineshima, Daisuke Bekki |
| Abstract | Sequence-to-sequence models have shown strong performance in a wide range of NLP tasks, yet their applications to sentence generation from logical representations are underdeveloped. In this paper, we present a sequence-to-sequence model for generating sentences from logical meaning representations based on event semantics. We use a semantic parsing system based on Combinatory Categorial Grammar (CCG) to obtain data annotated with logical formulas. We augment our sequence-to-sequence model with masking for predicates to constrain output sentences. We also propose a novel evaluation method for generation using Recognizing Textual Entailment (RTE). Combining parsing and generation, we test whether or not the output sentence entails the original text and vice versa. Experiments showed that our model outperformed a baseline with respect to both BLEU scores and accuracies in RTE. |
| Tasks | Machine Translation, Natural Language Inference, Semantic Parsing, Semantic Textual Similarity, Text Generation |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-6549/ |
| https://www.aclweb.org/anthology/W18-6549 | |
| PWC | https://paperswithcode.com/paper/neural-sentence-generation-from-formal |
| Repo | |
| Framework | |
A Model for Learned Bloom Filters and Optimizing by Sandwiching
| Title | A Model for Learned Bloom Filters and Optimizing by Sandwiching |
| Authors | Michael Mitzenmacher |
| Abstract | Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters, with the following outcomes: (1) we clarify what guarantees can and cannot be associated with such a structure; (2) we show how to estimate what size the learning function must obtain in order to obtain improved performance; (3) we provide a simple method, sandwiching, for optimizing learned Bloom filters; and (4) we propose a design and analysis approach for a learned Bloomier filter, based on our modeling approach. |
| Tasks | |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7328-a-model-for-learned-bloom-filters-and-optimizing-by-sandwiching |
| http://papers.nips.cc/paper/7328-a-model-for-learned-bloom-filters-and-optimizing-by-sandwiching.pdf | |
| PWC | https://paperswithcode.com/paper/a-model-for-learned-bloom-filters-and-1 |
| Repo | |
| Framework | |
Maximizing Induced Cardinality Under a Determinantal Point Process
| Title | Maximizing Induced Cardinality Under a Determinantal Point Process |
| Authors | Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet |
| Abstract | Determinantal point processes (DPPs) are well-suited to recommender systems where the goal is to generate collections of diverse, high-quality items. In the existing literature this is usually formulated as finding the mode of the DPP (the so-called MAP set). However, the MAP objective inherently assumes that the DPP models “optimal” recommendation sets, and yet obtaining such a DPP is nontrivial when there is no ready source of example optimal sets. In this paper we advocate an alternative framework for applying DPPs to recommender systems. Our approach assumes that the DPP simply models user engagements with recommended items, which is more consistent with how DPPs for recommender systems are typically trained. With this assumption, we are able to formulate a metric that measures the expected number of items that a user will engage with. We formalize this optimization of this metric as the Maximum Induced Cardinality (MIC) problem. Although the MIC objective is not submodular, we show that it can be approximated by a submodular function, and that empirically it is well-optimized by a greedy algorithm. |
| Tasks | Point Processes, Recommendation Systems |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7923-maximizing-induced-cardinality-under-a-determinantal-point-process |
| http://papers.nips.cc/paper/7923-maximizing-induced-cardinality-under-a-determinantal-point-process.pdf | |
| PWC | https://paperswithcode.com/paper/maximizing-induced-cardinality-under-a |
| Repo | |
| Framework | |
Anaphora Resolution with the ARRAU Corpus
| Title | Anaphora Resolution with the ARRAU Corpus |
| Authors | Massimo Poesio, Yulia Grishina, Varada Kolhatkar, Nafise Moosavi, Ina Roesiger, Adam Roussel, Fabian Simonjetz, Alex Uma, ra, Olga Uryupina, Juntao Yu, Heike Zinsmeister |
| Abstract | The ARRAU corpus is an anaphorically annotated corpus of English providing rich linguistic information about anaphora resolution. The most distinctive feature of the corpus is the annotation of a wide range of anaphoric relations, including bridging references and discourse deixis in addition to identity (coreference). Other distinctive features include treating all NPs as markables, including non-referring NPs; and the annotation of a variety of morphosyntactic and semantic mention and entity attributes, including the genericity status of the entities referred to by markables. The corpus however has not been extensively used for anaphora resolution research so far. In this paper, we discuss three datasets extracted from the ARRAU corpus to support the three subtasks of the CRAC 2018 Shared Task{–}identity anaphora resolution over ARRAU-style markables, bridging references resolution, and discourse deixis; the evaluation scripts assessing system performance on those datasets; and preliminary results on these three tasks that may serve as baseline for subsequent research in these phenomena. |
| Tasks | |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-0702/ |
| https://www.aclweb.org/anthology/W18-0702 | |
| PWC | https://paperswithcode.com/paper/anaphora-resolution-with-the-arrau-corpus |
| Repo | |
| Framework | |
Supervising Unsupervised Learning
| Title | Supervising Unsupervised Learning |
| Authors | Vikas Garg |
| Abstract | We introduce a framework to transfer knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms. We demonstrate the versatility of our framework via rigorous agnostic bounds on a variety of unsupervised problems. In the context of clustering, our approach helps choose the number of clusters and the clustering algorithm, remove the outliers, and provably circumvent Kleinberg’s impossibility result. Experiments across hundreds of problems demonstrate improvements in performance on unsupervised data with simple algorithms despite the fact our problems come from heterogeneous domains. Additionally, our framework lets us leverage deep networks to learn common features across many small datasets, and perform zero shot learning. |
| Tasks | Zero-Shot Learning |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7747-supervising-unsupervised-learning |
| http://papers.nips.cc/paper/7747-supervising-unsupervised-learning.pdf | |
| PWC | https://paperswithcode.com/paper/supervising-unsupervised-learning-1 |
| Repo | |
| Framework | |
A Common Framework for Interactive Texture Transfer
| Title | A Common Framework for Interactive Texture Transfer |
| Authors | Yifang Men, Zhouhui Lian, Yingmin Tang, Jianguo Xiao |
| Abstract | In this paper, we present a general-purpose solution to interactive texture transfer problems that better preserves both local structure and visual richness. It is challenging due to the diversity of tasks and the simplicity of required user guidance. The core idea of our common framework is to use multiple custom channels to dynamically guide the synthesis process. For interactivity, users can control the spatial distribution of stylized textures via semantic channels. The structure guidance, acquired by two stages of automatic extraction and propagation of structure information, provides a prior for initialization and preserves the salient structure by searching the nearest neighbor fields (NNF) with structure coherence. Meanwhile, texture coherence is also exploited to maintain similar style with the source image. In addition, we leverage an improved PatchMatch with extended NNF and matrix operations to obtain transformable source patches with richer geometric information at high speed. We demonstrate the effectiveness and superiority of our method on a variety of scenes through extensive comparisons with state-of-the-art algorithms. |
| Tasks | |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Men_A_Common_Framework_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Men_A_Common_Framework_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/a-common-framework-for-interactive-texture |
| Repo | |
| Framework | |
Role-specific Language Models for Processing Recorded Neuropsychological Exams
| Title | Role-specific Language Models for Processing Recorded Neuropsychological Exams |
| Authors | Tuka Al Hanai, Rhoda Au, James Glass |
| Abstract | Neuropsychological examinations are an important screening tool for the presence of cognitive conditions (e.g. Alzheimer{'}s, Parkinson{'}s Disease), and require a trained tester to conduct the exam through spoken interactions with the subject. While audio is relatively easy to record, it remains a challenge to automatically diarize (who spoke when?), decode (what did they say?), and assess a subject{'}s cognitive health. This paper demonstrates a method to determine the cognitive health (impaired or not) of 92 subjects, from audio that was diarized using an automatic speech recognition system trained on TED talks and on the structured language used by testers and subjects. Using leave-one-out cross validation and logistic regression modeling we show that even with noisily decoded data (81{%} WER) we can still perform accurate enough diarization (0.02{%} confusion rate) to determine the cognitive state of a subject (0.76 AUC). |
| Tasks | Epidemiology, Speaker Diarization, Speech Recognition |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-2117/ |
| https://www.aclweb.org/anthology/N18-2117 | |
| PWC | https://paperswithcode.com/paper/role-specific-language-models-for-processing |
| Repo | |
| Framework | |
Pluralizing Nouns across Agglutinating Bantu Languages
| Title | Pluralizing Nouns across Agglutinating Bantu Languages |
| Authors | Joan Byamugisha, C. Maria Keet, Brian DeRenzi |
| Abstract | Text generation may require the pluralization of nouns, such as in context-sensitive user interfaces and in natural language generation more broadly. While this has been solved for the widely-used languages, this is still a challenge for the languages in the Bantu language family. Pluralization results obtained for isiZulu and Runyankore showed there were similarities in approach, including the need to combine syntax with semantics, despite belonging to different language zones. This suggests that bootstrapping and generalizability might be feasible. We investigated this systematically for seven languages across three different Guthrie language zones. The first outcome is that Meinhof{'}s 1948 specification of the noun classes are indeed inadequate for computational purposes for all examined languages, due to non-determinism in prefixes, and we thus redefined the characteristic noun class tables of 29 noun classes into 53. The second main result is that the generic pluralizer achieved over 93{%} accuracy in coverage testing and over 94{%} on a random sample. This is comparable to the language-specific isiZulu and Runyankore pluralizers. |
| Tasks | Text Generation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1223/ |
| https://www.aclweb.org/anthology/C18-1223 | |
| PWC | https://paperswithcode.com/paper/pluralizing-nouns-across-agglutinating-bantu |
| Repo | |
| Framework | |
Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem
| Title | Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem |
| Authors | Alex Shvets, er, Simon Mille, Leo Wanner |
| Abstract | An increasing amount of research tackles the challenge of text generation from abstract ontological or semantic structures, which are in their very nature potentially large connected graphs. These graphs must be {``}packaged{''} into sentence-wise subgraphs. We interpret the problem of sentence packaging as a community detection problem with post optimization. Experiments on the texts of the VerbNet/FrameNet structure annotated-Penn Treebank, which have been converted into graphs by a coreference merge using Stanford CoreNLP, show a high F1-score of 0.738. | |
| Tasks | Community Detection, Text Generation, Text Simplification |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-6542/ |
| https://www.aclweb.org/anthology/W18-6542 | |
| PWC | https://paperswithcode.com/paper/sentence-packaging-in-text-generation-from |
| Repo | |
| Framework | |
Learning Multilingual Topics from Incomparable Corpora
| Title | Learning Multilingual Topics from Incomparable Corpora |
| Authors | Shudong Hao, Michael J. Paul |
| Abstract | Multilingual topic models enable crosslingual tasks by extracting consistent topics from multilingual corpora. Most models require parallel or comparable training corpora, which limits their ability to generalize. In this paper, we first demystify the knowledge transfer mechanism behind multilingual topic models by defining an alternative but equivalent formulation. Based on this analysis, we then relax the assumption of training data required by most existing models, creating a model that only requires a dictionary for training. Experiments show that our new method effectively learns coherent multilingual topics from partially and fully incomparable corpora with limited amounts of dictionary resources. |
| Tasks | Topic Models, Transfer Learning |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1220/ |
| https://www.aclweb.org/anthology/C18-1220 | |
| PWC | https://paperswithcode.com/paper/learning-multilingual-topics-from-1 |
| Repo | |
| Framework | |
Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis
| Title | Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis |
| Authors | Hiroyuki Kasai, Hiroyuki Sato, Bamdev Mishra |
| Abstract | Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite number of loss functions on a Riemannian manifold. The present paper proposes a Riemannian stochastic recursive gradient algorithm (R-SRG), which does not require the inverse of retraction between two distant iterates on the manifold. Convergence analyses of R-SRG are performed on both retraction-convex and non-convex functions under computationally efficient retraction and vector transport operations. The key challenge is analysis of the influence of vector transport along the retraction curve. Numerical evaluations reveal that R-SRG competes well with state-of-the-art Riemannian batch and stochastic gradient algorithms. |
| Tasks | |
| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=2109 |
| http://proceedings.mlr.press/v80/kasai18a/kasai18a.pdf | |
| PWC | https://paperswithcode.com/paper/riemannian-stochastic-recursive-gradient |
| Repo | |
| Framework | |
Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks
| Title | Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks |
| Authors | Brenden Lake, Marco Baroni |
| Abstract | Humans can understand and produce new utterances effortlessly, thanks to their systematic compositional skills. Once a person learns the meaning of a new verb “dax,” he or she can immediately understand the meaning of “dax twice” or “sing and dax.” In this paper, we introduce the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences. We then test the zero-shot generalization capabilities of a variety of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence methods. We find that RNNs can generalize well when the differences between training and test commands are small, so that they can apply “mix-and-match” strategies to solve the task. However, when generalization requires systematic compositional skills (as in the “dax” example above), RNNs fail spectacularly. We conclude with a proof-of-concept experiment in neural machine translation, supporting the conjecture that lack of systematicity is an important factor explaining why neural networks need very large training sets. |
| Tasks | Machine Translation |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=H18WqugAb |
| https://openreview.net/pdf?id=H18WqugAb | |
| PWC | https://paperswithcode.com/paper/still-not-systematic-after-all-these-years-on |
| Repo | |
| Framework | |
Neural Machine Translation Incorporating Named Entity
| Title | Neural Machine Translation Incorporating Named Entity |
| Authors | Arata Ugawa, Akihiro Tamura, Takashi Ninomiya, Hiroya Takamura, Manabu Okumura |
| Abstract | This study proposes a new neural machine translation (NMT) model based on the encoder-decoder model that incorporates named entity (NE) tags of source-language sentences. Conventional NMT models have two problems enumerated as follows: (i) they tend to have difficulty in translating words with multiple meanings because of the high ambiguity, and (ii) these models{'}abilitytotranslatecompoundwordsseemschallengingbecausetheencoderreceivesaword, a part of the compound word, at each time step. To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word. Furthermore,the encoder introduces a chunk-level LSTM layer over a word-level LSTM layer and hierarchically encodes a source-language sentence to capture a compound NE as a chunk on the basis of the NE tags. We evaluate the proposed model on an English-to-Japanese translation task with the ASPEC, and English-to-Bulgarian and English-to-Romanian translation tasks with the Europarl corpus. The evaluation results show that the proposed model achieves up to 3.11 point improvement in BLEU. |
| Tasks | Machine Translation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1274/ |
| https://www.aclweb.org/anthology/C18-1274 | |
| PWC | https://paperswithcode.com/paper/neural-machine-translation-incorporating |
| Repo | |
| Framework | |
Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers
| Title | Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers |
| Authors | Prakruthi Karuna, Hemant Purohit, {"O}zlem Uzuner, Sushil Jajodia, Rajesh Ganesan |
| Abstract | Ever increasing ransomware attacks and thefts of intellectual property demand cybersecurity solutions to protect critical documents. One emerging solution is to place fake text documents in the repository of critical documents for deceiving and catching cyber attackers. We can generate fake text documents by obscuring the salient information in legit text documents. However, the obscuring process can result in linguistic inconsistencies, such as broken co-references and illogical flow of ideas across the sentences, which can discern the fake document and render it unbelievable. In this paper, we propose a novel method to generate believable fake text documents by automatically improving the linguistic consistency of computer-generated fake text. Our method focuses on enhancing syntactic cohesion and semantic coherence across discourse segments. We conduct experiments with human subjects to evaluate the effect of believability improvements in distinguishing legit texts from fake texts. Results show that the probability to distinguish legit texts from believable fake texts is consistently lower than from fake texts that have not been improved in believability. This indicates the effectiveness of our method in generating believable fake text. |
| Tasks | Intrusion Detection, Text Generation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4104/ |
| https://www.aclweb.org/anthology/W18-4104 | |
| PWC | https://paperswithcode.com/paper/enhancing-cohesion-and-coherence-of-fake-text |
| Repo | |
| Framework | |
