title stringlengths 5 342 | author stringlengths 3 2.17k | year int64 1.95k 2.02k | abstract stringlengths 0 12.7k | pages stringlengths 1 702 | queryID stringlengths 4 40 | query stringlengths 1 300 | paperID stringlengths 0 40 | include int64 0 1 |
|---|---|---|---|---|---|---|---|---|
Improving Semantic Parsing via Answer Type Inference | Yavuz, Semih and
Gur, Izzeddin and
Su, Yu and
Srivatsa, Mudhakar and
Yan, Xifeng | 2,016 | nan | 149--159 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | f3594f9d60c98cac88f9033c69c2b666713ed6d6 | 1 |
{NLP} Infrastructure for the {L}ithuanian Language | Vitkut{\.e}-Ad{\v{z}}gauskien{\.e}, Daiva and
Utka, Andrius and
Amilevi{\v{c}}ius, Darius and
Krilavi{\v{c}}ius, Tomas | 2,016 | The Information System for Syntactic and Semantic Analysis of the Lithuanian language (lith. Lietuvi{\k{u}} kalbos sintaksin{\.e}s ir semantin{\.e}s analiz{\.e}s informacin{\.e} sistema, LKSSAIS) is the first infrastructure for the Lithuanian language combining Lithuanian language tools and resources for diverse lingui... | 2539--2542 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 3a1d0127f51e144c1c280c353e6b316681da7d4b | 0 |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing | Xiong, Wenhan and
Wu, Jiawei and
Lei, Deren and
Yu, Mo and
Chang, Shiyu and
Guo, Xiaoxiao and
Wang, William Yang | 2,019 | Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such techniques, however, are not directly applicable to more open and practical scenarios where the type set is not restricted by KB schema and ... | 773--784 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | a0713d945b2e5c2bdeeba68399c8ac6ea84e0ca6 | 1 |
A free/open-source rule-based machine translation system for {C}rimean {T}atar to {T}urkish | G{\"o}k{\i}rmak, Memduh and
Tyers, Francis and
Washington, Jonathan | 2,019 | nan | 24--31 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | a3c842456422ed20b8a78520710e414c9d51f756 | 0 |
Prompt-learning for Fine-grained Entity Typing | Ding, Ning and
Chen, Yulin and
Han, Xu and
Xu, Guangwei and
Wang, Xiaobin and
Xie, Pengjun and
Zheng, Haitao and
Liu, Zhiyuan and
Li, Juanzi and
Kim, Hong-Gee | 2,022 | As an effective approach to adapting pre-trained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers. By using cloze-style language prompts to stimulate the versatile knowledge of PLMs, prompt-learning can achieve promising results on a series of NLP tasks, ... | 6888--6901 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | bf722dc893ddaad5045fca5646212ec3badf3c5a | 1 |
Rethinking Positional Encoding in Tree Transformer for Code Representation | Peng, Han and
Li, Ge and
Zhao, Yunfei and
Jin, Zhi | 2,022 | Transformers are now widely used in code representation, and several recent works further develop tree Transformers to capture the syntactic structure in source code. Specifically, novel tree positional encodings have been proposed to incorporate inductive bias into Transformer.In this work, we propose a novel tree Tra... | 3204--3214 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 20333c34f892c8e0c2f4e6c37295a8b43ef35c02 | 0 |
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks | Jin, Hailong and
Hou, Lei and
Li, Juanzi and
Dong, Tiansi | 2,019 | This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base. We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem. We c... | 4969--4978 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 074e3497b03366caf2e17acd59fb1c52ccf8be55 | 1 |
{GAL}s: 基於對抗式學習之整列式摘要法 ({GAL}s: A {GAN}-based Listwise Summarizer) | Kuo, Chia-Chih and
Chen, Kuan-Yu | 2,019 | nan | 15--24 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | e20fa739fd0b4f000966f37801ddb3a685ce0c5c | 0 |
Type-Aware Distantly Supervised Relation Extraction with Linked Arguments | Koch, Mitchell and
Gilmer, John and
Soderland, Stephen and
Weld, Daniel S. | 2,014 | nan | 1891--1901 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | bd6b372f343ca16cbf97981967bf896bf2e351fd | 1 |
Dive deeper: Deep Semantics for Sentiment Analysis | Jadhav, Nikhilkumar and
Bhattacharyya, Pushpak | 2,014 | nan | 113--118 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 830746182677f9c1dc27c87222e01a0bb0ca4dab | 0 |
Can {NLI} Models Verify {QA} Systems{'} Predictions? | Chen, Jifan and
Choi, Eunsol and
Durrett, Greg | 2,021 | To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just {``}good enough{''} in the context of imperfect QA datasets. We explore the use of natural language inference (NLI) as a way to achieve this goal, as NLI inherently requires the premise (do... | 3841--3854 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | e3bba08dc07c5f1372b78450990ba0ef305a834c | 1 |
Adaptation de ressources en langue anglaise pour interroger des donn{\'e}es tabulaires en fran{\c{c}}ais (Adaptation of resources in {E}nglish to query {F}rench tabular data) | Blandin, Alexis | 2,021 | Les r{\'e}cents d{\'e}veloppements des approches d{'}apprentissage neuronal profond ont permis des avanc{\'e}es tr{\`e}s significatives dans le domaine de l{'}interrogation des syst{\`e}mes d{'}information en langage naturel. Cependant, pour le fran{\c{c}}ais, les ressources {\`a} disposition ne permettent de consid{\'... | 47--54 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 82e361c8f77753391e1f5245f346d13547e13309 | 0 |
Design Challenges for Entity Linking | Ling, Xiao and
Singh, Sameer and
Weld, Daniel S. | 2,015 | Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions... | 315--328 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | c6b53dd64d79a59f49f261baac8d2581a29ca06a | 1 |
Paraphrase Identification and Semantic Similarity in {T}witter with Simple Features | Vo, Ngoc Phuoc An and
Magnolini, Simone and
Popescu, Octavian | 2,015 | nan | 10--19 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | db11cb39e978d1423bdc5bbdeb29706d41368cca | 0 |
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation | Poliak, Adam and
Haldar, Aparajita and
Rudinger, Rachel and
Hu, J. Edward and
Pavlick, Ellie and
White, Aaron Steven and
Van Durme, Benjamin | 2,018 | We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in ... | 67--81 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | a609671e92b03a421236f2873b571159d7c2515c | 1 |
Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback | Lawrence, Carolin and
Riezler, Stefan | 2,018 | Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system. We show how to apply this learning framework to neural semantic parsing. From a machine learning perspective, the key challenge lies i... | 1820--1830 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 669e6be7cd92ba6bda39d9e3a030e72fde07a418 | 0 |
Improving Fine-grained Entity Typing with Entity Linking | Dai, Hongliang and
Du, Donghong and
Li, Xin and
Song, Yangqiu | 2,019 | Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. In this paper, we use entity linking to help with the fine-grained entity type classification process. We propose a deep neural model that makes predi... | 6210--6215 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | b74b272c7fe881614f3eb8c2504b037439571eec | 1 |
Hitachi at {MRP} 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing | Koreeda, Yuta and
Morio, Gaku and
Morishita, Terufumi and
Ozaki, Hiroaki and
Yanai, Kohsuke | 2,019 | This paper describes the proposed system of the Hitachi team for the Cross-Framework Meaning Representation Parsing (MRP 2019) shared task. In this shared task, the participating systems were asked to predict nodes, edges and their attributes for five frameworks, each with different order of {``}abstraction{''} from in... | 114--126 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 06835b411a20e424869fd3a6ce5c35a7082d5732 | 0 |
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss | Xu, Peng and
Barbosa, Denilson | 2,018 | The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt... | 16--25 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 008405f7ee96677ac23cc38be360832af2d9f437 | 1 |
{UKP}-Athene: Multi-Sentence Textual Entailment for Claim Verification | Hanselowski, Andreas and
Zhang, Hao and
Li, Zile and
Sorokin, Daniil and
Schiller, Benjamin and
Schulz, Claudia and
Gurevych, Iryna | 2,018 | The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, do... | 103--108 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 0c2790d4894940a5cf9084b09788a6c65617c209 | 0 |
Embedding Methods for Fine Grained Entity Type Classification | Yogatama, Dani and
Gillick, Daniel and
Lazic, Nevena | 2,015 | nan | 291--296 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | cd51e6faf377104269ba1e905ce430650677155c | 1 |
A Pilot Experiment on Exploiting Translations for Literary Studies on Kafka{'}s {``}Verwandlung{''} | Cap, Fabienne and
R{\"o}siger, Ina and
Kuhn, Jonas | 2,015 | nan | 48--57 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 1bf9dc4e79fb66afc9b9037ffaa71d2847191477 | 0 |
{T}axo{C}lass: Hierarchical Multi-Label Text Classification Using Only Class Names | Shen, Jiaming and
Qiu, Wenda and
Meng, Yu and
Shang, Jingbo and
Ren, Xiang and
Han, Jiawei | 2,021 | Hierarchical multi-label text classification (HMTC) aims to tag each document with a set of classes from a taxonomic class hierarchy. Most existing HMTC methods train classifiers using massive human-labeled documents, which are often too costly to obtain in real-world applications. In this paper, we explore to conduct ... | 4239--4249 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 15e100120f080b9ef4230b4cbb8e107b76e2b839 | 1 |
Self-supervised Regularization for Text Classification | Zhou, Meng and
Li, Zechen and
Xie, Pengtao | 2,021 | Text classification is a widely studied problem and has broad applications. In many real-world problems, the number of texts for training classification models is limited, which renders these models prone to overfitting. To address this problem, we propose SSL-Reg, a data-dependent regularization approach based on self... | 641--656 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 7da6d909368bbbaa355a129b0c2272ebdbd16a4c | 0 |
Grounding {`}Grounding{'} in {NLP} | Chandu, Khyathi Raghavi and
Bisk, Yonatan and
Black, Alan W | 2,021 | nan | 4283--4305 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 2550fafc0cbd8bbf7aadd864ac569596d33db038 | 1 |
Multi-Turn Target-Guided Topic Prediction with {M}onte {C}arlo Tree Search | Yang, Jingxuan and
Li, Si and
Guo, Jun | 2,021 | This paper concerns the problem of topic prediction in target-guided conversation, which requires the system to proactively and naturally guide the topic thread of the conversation, ending up with achieving a designated target subject. Existing studies usually resolve the task with a sequence of single-turn topic predi... | 324--334 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 8143632592e45f57d59257ac0e7cb9cb60634907 | 0 |
Fine-grained Entity Typing via Label Reasoning | Liu, Qing and
Lin, Hongyu and
Xiao, Xinyan and
Han, Xianpei and
Sun, Le and
Wu, Hua | 2,021 | Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowle... | 4611--4622 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 7f30821267a11138497107d947ea39726e4b7fbd | 1 |
Learning Disentangled Latent Topics for {T}witter Rumour Veracity Classification | Dougrez-Lewis, John and
Liakata, Maria and
Kochkina, Elena and
He, Yulan | 2,021 | nan | 3902--3908 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 6260975a9a50ab68f136ab79f4a912e253aa2680 | 0 |
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model | Dai, Hongliang and
Song, Yangqiu and
Wang, Haixun | 2,021 | Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions. A key challenge for this ultra-fine entity typing task is that human annotated data are extremely scarce,... | 1790--1799 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 70b49a024787d3ad374fb78dc87e3ba2b5e16566 | 1 |
{MRF}-Chat: Improving Dialogue with {M}arkov Random Fields | Grover, Ishaan and
Huggins, Matthew and
Breazeal, Cynthia and
Park, Hae Won | 2,021 | Recent state-of-the-art approaches in open-domain dialogue include training end-to-end deep-learning models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts in a knowledge graph and persona of the agent and the user, among others. While neural models... | 4925--4936 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 26a15c0e1becb323f40a616003a15db48ea1581a | 0 |
Modeling Fine-Grained Entity Types with Box Embeddings | Onoe, Yasumasa and
Boratko, Michael and
McCallum, Andrew and
Durrett, Greg | 2,021 | Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types{'} complex interdependencies. We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to capture hierarchie... | 2051--2064 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 176e3cbe3141c8b874df663711dca9b7470b8243 | 1 |
The Low-Dimensional Linear Geometry of Contextualized Word Representations | Hernandez, Evan and
Andreas, Jacob | 2,021 | Black-box probing models can reliably extract linguistic features like tense, number, and syntactic role from pretrained word representations. However, the manner in which these features are encoded in representations remains poorly understood. We present a systematic study of the linear geometry of contextualized word... | 82--93 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 761f1607b380df54546fd2114b458aa19109cd3d | 0 |
Syntax-Enhanced Pre-trained Model | Xu, Zenan and
Guo, Daya and
Tang, Duyu and
Su, Qinliang and
Shou, Linjun and
Gong, Ming and
Zhong, Wanjun and
Quan, Xiaojun and
Jiang, Daxin and
Duan, Nan | 2,021 | We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the n... | 5412--5422 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 634e8fbeba53d45828846dd541ce0a0078c57b68 | 1 |
Enhancing Transformers with Gradient Boosted Decision Trees for {NLI} Fine-Tuning | Minixhofer, Benjamin and
Gritta, Milan and
Iacobacci, Ignacio | 2,021 | nan | 303--313 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 6d2773a788067dcce7ac6a82019649528d341b4e | 0 |
Interpretable Entity Representations through Large-Scale Typing | Onoe, Yasumasa and
Durrett, Greg | 2,020 | In standard methodology for natural language processing, entities in text are typically embedded in dense vector spaces with pre-trained models. The embeddings produced this way are effective when fed into downstream models, but they require end-task fine-tuning and are fundamentally difficult to interpret. In this pap... | 612--624 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 782a50a48ba5d32839631254285d989bfadfd193 | 1 |
基于对话约束的回复生成研究(Research on Response Generation via Dialogue Constraints) | Guan, Mengyu and
Wang, Zhongqing and
Li, Shoushan and
Zhou, Guodong | 2,020 | 现有的对话系统中存在着生成{``}好的{''}、{``}我不知道{''}等无意义的安全回复问题。日常对话中,对话者通常围绕特定的主题进行讨论且每句话都有明显的情感和意图。因此该文提出了基于对话约束的回复生成模型,即在Seq2Seq模型的基础上,结合对对话的主题、情感、意图的识别。该方法对生成回复的主题、情感和意图进行约束,从而生成具有合理的情感和意图且与对话主题相关的回复。实验证明,该文提出的方法能有效地提高生成回复的质量。 | 225--235 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | a7a965d5d8e6addd2cef51201fbab44134ca3c3f | 0 |
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start | Yin, Wenpeng and
Rajani, Nazneen Fatema and
Radev, Dragomir and
Socher, Richard and
Xiong, Caiming | 2,020 | A standard way to address different NLP problems is by first constructing a problem-specific dataset, then building a model to fit this dataset. To build the ultimate artificial intelligence, we desire a single machine that can handle diverse new problems, for which task-specific annotations are limited. We bring up te... | 8229--8239 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | e2d38543bd3cf813c63df336b21b003156ed48a8 | 1 |
The {N}iu{T}rans System for {WNGT} 2020 Efficiency Task | Hu, Chi and
Li, Bei and
Li, Yinqiao and
Lin, Ye and
Li, Yanyang and
Wang, Chenglong and
Xiao, Tong and
Zhu, Jingbo | 2,020 | This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task. We focus on the efficient implementation of deep Transformer models (Wang et al., 2019; Li et al., 2019) using NiuTensor, a flexible toolkit for NLP tasks. We explored the combination of deep encoder and shallow decoder i... | 204--210 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 18f86146a095a30d57569197c04f807bb10064e8 | 0 |
{FASTMATCH}: Accelerating the Inference of {BERT}-based Text Matching | Pang, Shuai and
Ma, Jianqiang and
Yan, Zeyu and
Zhang, Yang and
Shen, Jianping | 2,020 | Recently, pre-trained language models such as BERT have shown state-of-the-art accuracies in text matching. When being applied to IR (or QA), the BERT-based matching models need to online calculate the representations and interactions for all query-candidate pairs. The high inference cost has prohibited the deployments... | 6459--6469 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | e349b4f2061ba72cd693787d933e8fdb42eea23b | 1 |
A Multi-task Learning Framework for Opinion Triplet Extraction | Zhang, Chen and
Li, Qiuchi and
Song, Dawei and
Wang, Benyou | 2,020 | The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of a... | 819--828 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 0c708c78e7c4b1d94b5c64f3469a58770995dc4d | 0 |
Hierarchical Entity Typing via Multi-level Learning to Rank | Chen, Tongfei and
Chen, Yunmo and
Van Durme, Benjamin | 2,020 | We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree. During prediction, we define a coarse-to-fin... | 8465--8475 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | d4b4484308fa6efd821ad8084cc9bde9d3f211b0 | 1 |
Automated Assessment of Noisy Crowdsourced Free-text Answers for {H}indi in Low Resource Setting | Agarwal, Dolly and
Gupta, Somya and
Baghel, Nishant | 2,020 | The requirement of performing assessments continually on a larger scale necessitates the implementation of automated systems for evaluation of the learners{'} responses to free-text questions. We target children of age group 8-14 years and use an ASR integrated assessment app to crowdsource learners{'} responses to fre... | 122--131 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 60bdd5a0b82b3c9b399868b3fc16d192171951f4 | 0 |
Description-Based Zero-shot Fine-Grained Entity Typing | Obeidat, Rasha and
Fern, Xiaoli and
Shahbazi, Hamed and
Tadepalli, Prasad | 2,019 | Fine-grained Entity typing (FGET) is the task of assigning a fine-grained type from a hierarchy to entity mentions in the text. As the taxonomy of types evolves continuously, it is desirable for an entity typing system to be able to recognize novel types without additional training. This work proposes a zero-shot entit... | 807--814 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 51b958dd76a6aefcd521ec0f503c3e334f711362 | 1 |
Lattice-Based Transformer Encoder for Neural Machine Translation | Xiao, Fengshun and
Li, Jiangtong and
Zhao, Hai and
Wang, Rui and
Chen, Kehai | 2,019 | Neural machine translation (NMT) takes deterministic sequences for source representations. However, either word-level or subword-level segmentations have multiple choices to split a source sequence with different word segmentors or different subword vocabulary sizes. We hypothesize that the diversity in segmentations m... | 3090--3097 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 0ab0fda8774c303be8f8f8c8f684a890dcf5d455 | 0 |
Learning to Denoise Distantly-Labeled Data for Entity Typing | Onoe, Yasumasa and
Durrett, Greg | 2,019 | Distantly-labeled data can be used to scale up training of statistical models, but it is typically noisy and that noise can vary with the distant labeling technique. In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and de... | 2407--2417 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | dc138300b87f5bfccec609644d5edc08c4d783e9 | 1 |
Unbounded Stress in Subregular Phonology | Hao, Yiding and
Andersson, Samuel | 2,019 | This paper situates culminative unbounded stress systems within the subregular hierarchy for functions. While Baek (2018) has argued that such systems can be uniformly understood as input tier-based strictly local constraints, we show here that default-to-opposite-side and default-to-same-side stress systems belong to ... | 135--143 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 7bcfa8384622e15a40f25e1b388485f8c09c1aec | 0 |
Ultra-Fine Entity Typing | Choi, Eunsol and
Levy, Omer and
Choi, Yejin and
Zettlemoyer, Luke | 2,018 | We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head wor... | 87--96 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 4157834ed2d2fea6b6f652a72a9d0487edbc9f57 | 1 |
System Description of Supervised and Unsupervised Neural Machine Translation Approaches from {``}{NL} Processing{''} Team at {D}eep{H}ack.{B}abel Task | Gusev, Ilya and
Oboturov, Artem | 2,018 | nan | 45--52 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | f3537ff7aeb8e406369804b4c29b5f05ba5b1473 | 0 |
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures | Vilnis, Luke and
Li, Xiang and
Murty, Shikhar and
McCallum, Andrew | 2,018 | Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE), are a natural way to model transitive relational data (e.g. entailment graphs). However, OE learns a deterministic knowledge base, limiting expressiveness of queries and the ability to use uncerta... | 263--272 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | c6e0d70a81a83143d1f3b220d0941843ca03ca71 | 1 |
Multi-Task Neural Models for Translating Between Styles Within and Across Languages | Niu, Xing and
Rao, Sudha and
Carpuat, Marine | 2,018 | Generating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer and formality-sensitive machine translation. We propose to solve these tasks jointly using multi-task learning, and show that our models ac... | 1008--1021 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | e96b79eeb009ddceff50b4e864b1ee2edaf3ca6c | 0 |
Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework | White, Aaron Steven and
Rastogi, Pushpendre and
Duh, Kevin and
Van Durme, Benjamin | 2,017 | We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input s... | 996--1005 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 4546b7207e1a87c205bdf45c70f7b06fb3c38e21 | 1 |
Enhancing Machine Translation of Academic Course Catalogues with Terminological Resources | Scansani, Randy and
Bernardini, Silvia and
Ferraresi, Adriano and
Gaspari, Federico and
Soffritti, Marcello | 2,017 | This paper describes an approach to translating course unit descriptions from Italian and German into English, using a phrase-based machine translation (MT) system. The genre is very prominent among those requiring translation by universities in European countries in which English is a non-native language. For each lan... | 1--10 | ef25f1586cf6630f4a30d41ee5a2848b064dede3 | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | 37c67bbb4c3fd0c3b9bd35fb62671e0f5ddfe4a1 | 0 |
Improving Semantic Parsing via Answer Type Inference | Yavuz, Semih and
Gur, Izzeddin and
Su, Yu and
Srivatsa, Mudhakar and
Yan, Xifeng | 2,016 | nan | 149--159 | 4c75564731f564e78cafc76e18739bbcf4fceeb2 | Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding | f3594f9d60c98cac88f9033c69c2b666713ed6d6 | 1 |
Integrating Optical Character Recognition and Machine Translation of Historical Documents | Afli, Haithem and
Way, Andy | 2,016 | Machine Translation (MT) plays a critical role in expanding capacity in the translation industry. However, many valuable documents, including digital documents, are encoded in non-accessible formats for machine processing (e.g., Historical or Legal documents). Such documents must be passed through a process of Optical ... | 109--116 | 4c75564731f564e78cafc76e18739bbcf4fceeb2 | Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding | 82ccd8e2fa7b3e49d113e3abe194ecd4aa1e88f4 | 0 |
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge | Hao, Yanchao and
Zhang, Yuanzhe and
Liu, Kang and
He, Shizhu and
Liu, Zhanyi and
Wu, Hua and
Zhao, Jun | 2,017 | With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowledge. Meanwhile, as the neural network-based (NN-based) methods develop, NN-based... | 221--231 | 4c75564731f564e78cafc76e18739bbcf4fceeb2 | Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding | e9287b896a1c7360567915c3932b8df1ee4a81f7 | 1 |
Learning User Embeddings from Emails | Song, Yan and
Lee, Chia-Jung | 2,017 | Many important email-related tasks, such as email classification or search, highly rely on building quality document representations (e.g., bag-of-words or key phrases) to assist matching and understanding. Despite prior success on representing textual messages, creating quality user representations from emails was ove... | 733--738 | 4c75564731f564e78cafc76e18739bbcf4fceeb2 | Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding | 4e6bd3eeb15413a22cb611be2770a632b31a1951 | 0 |
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases | Chen, Yu and
Wu, Lingfei and
Zaki, Mohammed J. | 2,019 | When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles. However, most existing embedding-based methods for knowledge base question answering (KBQA) ignore the subtle inter-relationships between the question and the KB (e.g., entity types, ... | 2913--2923 | 4c75564731f564e78cafc76e18739bbcf4fceeb2 | Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding | c4cc66e3652a6c3bb4d1737fea2f50bdb3fe3a70 | 1 |
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings | Artetxe, Mikel and
Schwenk, Holger | 2,019 | Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we propose a new method for this task based on multilingual sentence embeddings. In contrast to previous approaches, which rely... | 3197--3203 | 4c75564731f564e78cafc76e18739bbcf4fceeb2 | Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding | 30b09a853ab72e53078f1feefe6de5a847a2b169 | 0 |
Data Recombination for Neural Semantic Parsing | Jia, Robin and
Liang, Percy | 2,016 | nan | 12--22 | 4c75564731f564e78cafc76e18739bbcf4fceeb2 | Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding | b7eac64a8410976759445cce235469163d23ee65 | 1 |
Global Open Resources and Information for Language and Linguistic Analysis ({GORILLA}) | Cavar, Damir and
Cavar, Malgorzata and
Moe, Lwin | 2,016 | The infrastructure Global Open Resources and Information for Language and Linguistic Analysis (GORILLA) was created as a resource that provides a bridge between disciplines such as documentary, theoretical, and corpus linguistics, speech and language technologies, and digital language archiving services. GORILLA is des... | 4484--4491 | 4c75564731f564e78cafc76e18739bbcf4fceeb2 | Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding | dae080f583ade82375888342fad6af00b4dfaa67 | 0 |
Question Answering on {F}reebase via Relation Extraction and Textual Evidence | Xu, Kun and
Reddy, Siva and
Feng, Yansong and
Huang, Songfang and
Zhao, Dongyan | 2,016 | nan | 2326--2336 | 4c75564731f564e78cafc76e18739bbcf4fceeb2 | Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding | e3919e94c811fd85f5038926fa354619861674f9 | 1 |
A {H}ungarian Sentiment Corpus Manually Annotated at Aspect Level | Szab{\'o}, Martina Katalin and
Vincze, Veronika and
Simk{\'o}, Katalin Ilona and
Varga, Viktor and
Hangya, Viktor | 2,016 | In this paper we present a Hungarian sentiment corpus manually annotated at aspect level. Our corpus consists of Hungarian opinion texts written about different types of products. The main aim of creating the corpus was to produce an appropriate database providing possibilities for developing text mining software tools... | 2873--2878 | 4c75564731f564e78cafc76e18739bbcf4fceeb2 | Knowledge Base Question Answering Based on Multi-head Attention Mechanism and Relative Position Coding | e627e852ca665fd2acc843807b61fc9a6a117a68 | 0 |
Improving Semantic Parsing via Answer Type Inference | Yavuz, Semih and
Gur, Izzeddin and
Su, Yu and
Srivatsa, Mudhakar and
Yan, Xifeng | 2,016 | nan | 149--159 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | f3594f9d60c98cac88f9033c69c2b666713ed6d6 | 1 |
Graph-Based Induction of Word Senses in {C}roatian | Bekavac, Marko and
{\v{S}}najder, Jan | 2,016 | Word sense induction (WSI) seeks to induce senses of words from unannotated corpora. In this paper, we address the WSI task for the Croatian language. We adopt the word clustering approach based on co-occurrence graphs, in which senses are taken to correspond to strongly inter-connected components of co-occurring words... | 3014--3018 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | d0de3456691c9b0b311719cbd76d2df9ee060497 | 0 |
Paraphrase-Driven Learning for Open Question Answering | Fader, Anthony and
Zettlemoyer, Luke and
Etzioni, Oren | 2,013 | nan | 1608--1618 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | c0be2ac2f45681f1852fc1d298af5dceb85834f4 | 1 |
Proceedings of the International Conference Recent Advances in Natural Language Processing {RANLP} 2013 | nan | 2,013 | nan | nan | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | f24cc8bb580d451500e57fd1857d6f6907ac3140 | 0 |
Question Answering over {F}reebase with Multi-Column Convolutional Neural Networks | Dong, Li and
Wei, Furu and
Zhou, Ming and
Xu, Ke | 2,015 | nan | 260--269 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 1ef01e7bfab2041bc0c0a56a57906964df9fc985 | 1 |
{LORIA} System for the {WMT}15 Quality Estimation Shared Task | Langlois, David | 2,015 | nan | 323--329 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | cb4ee7bf3069695ea9b8802a2c1cd76b6cc73d0c | 0 |
{CFO}: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases | Dai, Zihang and
Li, Lei and
Xu, Wei | 2,016 | nan | 800--810 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 76d28a1f4c52b2fbb798501e479023c4075b4803 | 1 |
Towards Lexical Encoding of Multi-Word Expressions in {S}panish Dialects | Bogantes, Diana and
Rodr{\'\i}guez, Eric and
Arauco, Alejandro and
Rodr{\'\i}guez, Alejandro and
Savary, Agata | 2,016 | This paper describes a pilot study in lexical encoding of multi-word expressions (MWEs) in 4 Latin American dialects of Spanish: Costa Rican, Colombian, Mexican and Peruvian. We describe the variability of MWE usage across dialects. We adapt an existing data model to a dialect-aware encoding, so as to represent dialect... | 2255--2261 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | c464069a72a9b170eb9e99f31a8d5b6f4906a84d | 0 |
Investigating Entity Knowledge in {BERT} with Simple Neural End-To-End Entity Linking | Broscheit, Samuel | 2,019 | A typical architecture for end-to-end entity linking systems consists of three steps: mention detection, candidate generation and entity disambiguation. In this study we investigate the following questions: (a) Can all those steps be learned jointly with a model for contextualized text-representations, i.e. BERT? (b) H... | 677--685 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 399308fa54ade9b1362d56628132323489ce50cd | 1 |
{LINSPECTOR} {WEB}: A Multilingual Probing Suite for Word Representations | Eichler, Max and
{\c{S}}ahin, G{\"o}zde G{\"u}l and
Gurevych, Iryna | 2,019 | We present LINSPECTOR WEB , an open source multilingual inspector to analyze word representations. Our system provides researchers working in low-resource settings with an easily accessible web based probing tool to gain quick insights into their word embeddings especially outside of the English language. To do this we... | 127--132 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 63bc1232a882cecdf2b939be4b563a82fef2f63e | 0 |
Simple Question Answering by Attentive Convolutional Neural Network | Yin, Wenpeng and
Yu, Mo and
Xiang, Bing and
Zhou, Bowen and
Sch{\"u}tze, Hinrich | 2,016 | This work focuses on answering single-relation factoid questions over Freebase. Each question can acquire the answer from a single fact of form (subject, predicate, object) in Freebase. This task, simple question answering (SimpleQA), can be addressed via a two-step pipeline: entity linking and fact selection. In fact ... | 1746--1756 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 812034099dd66df95a9f4ff741e17df62916ef4c | 1 |
Feature Derivation for Exploitation of Distant Annotation via Pattern Induction against Dependency Parses | Freitag, Dayne and
Niekrasz, John | 2,016 | nan | 36--45 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 1b4bc749341282b67277492034f26c011ee1761a | 0 |
Large-scale Semantic Parsing without Question-Answer Pairs | Reddy, Siva and
Lapata, Mirella and
Steedman, Mark | 2,014 | In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we con... | 377--392 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 34b2fb4d05b80cb73d0be3c855f7b236fbc3640c | 1 |
A Vague Sense Classifier for Detecting Vague Definitions in Ontologies | Alexopoulos, Panos and
Pavlopoulos, John | 2,014 | nan | 33--37 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 5abeb622ed1fb738bab8b759038670b7b082f966 | 0 |
{F}reebase {QA}: Information Extraction or Semantic Parsing? | Yao, Xuchen and
Berant, Jonathan and
Van Durme, Benjamin | 2,014 | nan | 82--86 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | b75329489baf067e6f7bbb74f16ffd49fba80dfa | 1 |
Domain Adaptation with Active Learning for Coreference Resolution | Zhao, Shanheng and
Ng, Hwee Tou | 2,014 | nan | 21--29 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 9ee289dbda34e13e0e35df9b343738b107fd7ce6 | 0 |
Semantic Parsing via Paraphrasing | Berant, Jonathan and
Liang, Percy | 2,014 | nan | 1415--1425 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 3d1d42c9435b419ac928ebf7bcf4c86a460d6ef4 | 1 |
A Recursive Recurrent Neural Network for Statistical Machine Translation | Liu, Shujie and
Yang, Nan and
Li, Mu and
Zhou, Ming | 2,014 | nan | 1491--1500 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 5d43224147a5bb8b17b6a6fc77bf86490e86991a | 0 |
No Need to Pay Attention: Simple Recurrent Neural Networks Work! | Ture, Ferhan and
Jojic, Oliver | 2,017 | First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65{\%}{--}76{\%} accuracy on benchmark sets. O... | 2866--2872 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | c3efd9334114f82644ed14c4b6083defc6209d85 | 1 |
{W}atset: Automatic Induction of Synsets from a Graph of Synonyms | Ustalov, Dmitry and
Panchenko, Alexander and
Biemann, Chris | 2,017 | This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the d... | 1579--1590 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | ba00cbd314dc52b299a8b0c34f1887bcd43cdc12 | 0 |
Character-Level Question Answering with Attention | He, Xiaodong and
Golub, David | 2,016 | nan | 1598--1607 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 698d675ba7134ac701de810c9ca4a6de72cb414b | 1 |
Towards a Linguistic Ontology with an Emphasis on Reasoning and Knowledge Reuse | Parvizi, Artemis and
Kohl, Matt and
Gonz{\`a}lez, Meritxell and
Saur{\'\i}, Roser | 2,016 | The Dictionaries division at Oxford University Press (OUP) is aiming to model, integrate, and publish lexical content for 100 languages focussing on digitally under-represented languages. While there are multiple ontologies designed for linguistic resources, none had adequate features for meeting our requirements, chie... | 441--448 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | da294532e499a663d36be2ab92a45111c4e5194e | 0 |
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems | Wang, Jian and
Liu, Junhao and
Bi, Wei and
Liu, Xiaojiang and
He, Kejing and
Xu, Ruifeng and
Yang, Min | 2,020 | Existing end-to-end task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which ma... | 4100--4110 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | d8d1bba29ee07abcc0586d5cbe056d11f8041077 | 1 |
Benefits of Intermediate Annotations in Reading Comprehension | Dua, Dheeru and
Singh, Sameer and
Gardner, Matt | 2,020 | Complex compositional reading comprehension datasets require performing latent sequential decisions that are learned via supervision from the final answer. A large combinatorial space of possible decision paths that result in the same answer, compounded by the lack of intermediate supervision to help choose the right p... | 5627--5634 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | f4874bd968b785cb9fceeccf26c333567a2b8dca | 0 |
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents | Chen, Daoyuan and
Li, Yaliang and
Lei, Kai and
Shen, Ying | 2,020 | Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts. In this paper, we consider the problem of shifted label distribution, which is caused by the inconsistency between the noisy-labeled trainin... | 5940--5950 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | c07d534742b7c8c88a7483fd9c98bdcbf9cbcbc6 | 1 |
Improving Transformer Models by Reordering their Sublayers | Press, Ofir and
Smith, Noah A. and
Levy, Omer | 2,020 | Multilayer transformer networks consist of interleaved self-attention and feedforward sublayers. Could ordering the sublayers in a different pattern lead to better performance? We generate randomly ordered transformers and train them with the language modeling objective. We observe that some of these models are able to... | 2996--3005 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 3ff8d265f4351e4b1fdac5b586466bee0b5d6fff | 0 |
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs | Tu, Ming and
Wang, Guangtao and
Huang, Jing and
Tang, Yun and
He, Xiaodong and
Zhou, Bowen | 2,019 | Multi-hop reading comprehension (RC) across documents poses new challenge over single-document RC because it requires reasoning over multiple documents to reach the final answer. In this paper, we propose a new model to tackle the multi-hop RC problem. We introduce a heterogeneous graph with different types of nodes an... | 2704--2713 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 3b7d05fc6e1e0a622f9a8772f4557a166f811698 | 1 |
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations | Chen, Mingda and
Tang, Qingming and
Wiseman, Sam and
Gimpel, Kevin | 2,019 | We propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics. We show we can achieve better disentanglement between semantic and syntactic representations by training with multiple losses, including losses... | 2453--2464 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | f716ed53188d5bc09c7b18ba35cb6e21ffb0273a | 0 |
{UH}op: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering | Chen, Zi-Yuan and
Chang, Chih-Hung and
Chen, Yi-Pei and
Nayak, Jijnasa and
Ku, Lun-Wei | 2,019 | In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called {``}one hop{''}. In related work, an exhaustive search from all one-hop relations, two-hop relations, and so on to the max-hop relations in the knowledge graph is necessary but expe... | 345--356 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 4e4230418470efbb3a86d407d8eedf329f361f6d | 1 |
Hybrid {RNN} at {S}em{E}val-2019 Task 9: Blending Information Sources for Domain-Independent Suggestion Mining | Ezen-Can, Aysu and
Can, Ethem F. | 2,019 | Social media has an increasing amount of information that both customers and companies can benefit from. These social media posts can include Tweets or be in the form of vocalization of complements and complaints (e.g., reviews) of a product or service. Researchers have been actively mining this invaluable information ... | 1199--1203 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 6bef349cfcc7b58206b41823fa08ada09e434c8a | 0 |
{CNN} for Text-Based Multiple Choice Question Answering | Chaturvedi, Akshay and
Pandit, Onkar and
Garain, Utpal | 2,018 | The task of Question Answering is at the very core of machine comprehension. In this paper, we propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. Given an article and a multiple choice question, our model assigns a scor... | 272--277 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 1baa640feacca6806b7cb58f17c885b6865f337b | 1 |
Searching for the {X}-Factor: Exploring Corpus Subjectivity for Word Embeddings | Tkachenko, Maksim and
Chia, Chong Cher and
Lauw, Hady | 2,018 | We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this ... | 1212--1221 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 0324fdde1d8702da4ecaed7fb61f027f7ef58795 | 0 |
Cooperative Denoising for Distantly Supervised Relation Extraction | Lei, Kai and
Chen, Daoyuan and
Li, Yaliang and
Du, Nan and
Yang, Min and
Fan, Wei and
Shen, Ying | 2,018 | Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art... | 426--436 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | c233e45922d3cf06c8a90ed4b28f045ec2e205fb | 1 |
Zero-shot Relation Classification as Textual Entailment | Obamuyide, Abiola and
Vlachos, Andreas | 2,018 | 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 (i... | 72--78 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 312b12dd6aa558b92df3ddd9b1057aa80a0ad718 | 0 |
Improved Neural Relation Detection for Knowledge Base Question Answering | Yu, Mo and
Yin, Wenpeng and
Hasan, Kazi Saidul and
dos Santos, Cicero and
Xiang, Bing and
Zhou, Bowen | 2,017 | Relation detection is a core component of many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning which detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to com... | 571--581 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 10b5dc51b61795718f79f3b4c9b5bbba44d252c0 | 1 |
Modelling semantic acquisition in second language learning | Kochmar, Ekaterina and
Shutova, Ekaterina | 2,017 | Using methods of statistical analysis, we investigate how semantic knowledge is acquired in English as a second language and evaluate the pace of development across a number of predicate types and content word combinations, as well as across the levels of language proficiency and native languages. Our exploratory study... | 293--302 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | a690dca513cee03a535e2cfa2b6026152cb5c81e | 0 |
Learning Hybrid Representations to Retrieve Semantically Equivalent Questions | dos Santos, C{\'\i}cero and
Barbosa, Luciano and
Bogdanova, Dasha and
Zadrozny, Bianca | 2,015 | nan | 694--699 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | 728aa52045cedce0ffb11975d880c7046abef3f2 | 1 |
Suitability of {P}ar{T}es Test Suite for Parsing Evaluation | Lloberes, Marina and
Castell{\'o}n, Irene and
Padr{\'o}, Llu{\'\i}s | 2,015 | nan | 61--65 | 4f929eb557a990cd3062c86c4be157909742245d | Knowledge-Based Reasoning Network for Relation Detection | c3448d9911e9a169f901617d8b74cb1bc8aa3c23 | 0 |
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