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Dilek Hakkani-Tur, Yun-Cheng Ju, Geoffrey Zweig, and Gokhan Tur

Spoken language understanding (SLU) in today’s conversational systems focuses on recognizing a set of domains, intents, and associated arguments, that are determined by application developers. User requests that are not covered by these are usually directed to search engines, and may remain unhandled. We propose a method that aims to find common user intents amongst these uncovered, out-of-domain utterances, with the goal of supporting future phases of dialog system design. Our approach relies on...

Publication details
Date: 1 September 2015
Type: Inproceeding
Publisher: Interspeech 2015 Conference
Young-Bum Kim, Ruhi Sarikaya, and Minwoo Jeong

In natural language understanding (NLU), a user utterance can be labeled differently depending on the domain or application (e.g., weather vs. calendar). Standard domain adaptation techniques are not directly applicable to take advantage of the existing annotations because they assume that the label set is invariant. We propose a solution based on label embeddings induced from canonical correlation analysis (CCA) that reduces the problem to a standard domain adaptation task and allows use of a number of...

Publication details
Date: 29 August 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Young-Bum Kim and Ruhi Sarikaya

In this paper, we apply the concept of pre-training to hidden-unit conditional random
fields (HUCRFs) to enable learning on unlabeled data. We present a simple yet effective pre-training technique that learns to associate words with their clusters, which are obtained in an unsupervised manner. The learned parameters are then used to initialize the supervised learning process. We also propose a word clustering technique based on canonical correlation analysis (CCA) that is sensitive to multiple word...

Publication details
Date: 28 August 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Young-Bum Kim, Xiaohu Liu, and Ruhi Sarikaya

In this paper, we introduce the task of selecting compact lexicon from large, noisy gazetteers.
This scenario arises often in practice, in particular spoken language understanding (SLU).
We propose a simple and effective solution based on matrix decomposition techniques:
canonical correlation analysis (CCA) and rank-revealing QR (RRQR) factorization. CCA is first used to derive low-dimensional gazetteer embeddings from domain-specific search logs. Then RRQR is used to find a subset of...

Publication details
Date: 27 August 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Kristina Toutanova, Waleed Ammar, Pallavi Chourdhury, and Hoifung Poon

Model selection (picking, for example, the feature set and the regularization strength) is crucial for building high-accuracy NLP models. In supervised learning, we can estimate the accuracy of a model on a subset of the labeled data and choose the model with the highest accuracy.
In contrast, here we focus on type-supervised learning, which uses constraints over the possible labels for word types for supervision, and labeled data is either not available or very small. For the setting where no...

Publication details
Date: 30 July 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Kristina Toutanova and Danqi Chen

In this paper we show the surprising effectiveness of a simple observed features model in comparison to latent feature models on two benchmark knowledge base completion datasets – FB15K and WN18. We also compare latent and observed feature models on a more challenging dataset derived from FB15K, and additionally coupled with textual mentions from a web-scale corpus. We show that the observed features model is most effective at capturing the information present for entity pairs with textual relations,...

Publication details
Date: 30 July 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, and Margaret Mitchell

Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional neural network (CNN) trained on images, and then a maximum entropy (ME) language model is used to arrange these words into a coherent sentence. The second uses the penultimate activation layer of the CNN as input to a recurrent neural network (RNN) that then generates the caption sequence. In this paper, we compare the merits...

Publication details
Date: 1 July 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Publication details
Date: 1 July 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Igor Labutov, sumit basu, and lucy vanderwende

We develop an approach for generating deep (i.e, high-level) comprehension questions from novel text that bypasses the myriad challenges of creating a full semantic representation. We do this by decomposing the task into an ontology-crowd-relevance workflow, consisting of first representing the original text in a low-dimensional ontology, then crowd-sourcing candidate question templates aligned with that space, and finally ranking potentially relevant templates for a novel region of text. If ontological...

Publication details
Date: 1 July 2015
Type: Inproceeding
Publisher: to appear in: Proceedings of ACL 2015
Ankur P. Parikh, Hoifung Poon, and Kristina Toutanova

Publication details
Date: 1 June 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Jialu Liu, Jingbo Shang, Chi Wang, Xiang Ren, and Jiawei Han

Text data are ubiquitous and play an essential role in big data applications. However, text data are mostly unstructured. Transforming unstructured text into structured units (e.g., semantically meaningful phrases) will substantially reduce semantic ambiguity and enhance the power and efficiency at manipulating such data using database technology. Thus mining quality phrases is a critical research problem in the field of databases. In this paper, we propose a new framework that extracts quality...

Publication details
Date: 1 June 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollar, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John Platt, Lawrence Zitnick, and Geoffrey Zweig

This paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dataset of image captions. We use multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives. The word detector outputs serve as conditional inputs to a maximum-entropy language model. The language model learns from...

Publication details
Date: 1 June 2015
Type: Article
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Meg Mitchell, Jian-Yun Nie, Jianfeng Gao, and Bill Dolan

We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information...

Publication details
Date: 1 June 2015
Type: Inproceeding
Publisher: Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL-HLT 2015)
Lucy Vanderwende, Arul Menezes, and Chris Quirk

In this demonstration, we will present our online parser that allows users to submit any sentence and obtain an analysis following the specification of AMR (Banarescu et al., 2014) to a large extent. This AMR analysis is generated by a small set of rules that convert a native Logical Form analysis provided by a pre-existing parser (see Vanderwende, 2015) into the AMR format. While we demonstrate the performance of our AMR parser on data sets annotated by the LDC, we will focus attention in the demo on...

Publication details
Date: 1 June 2015
Type: Inproceeding
Publisher: Proceedings of NAACL 2015
Young-Bum Kim, Minwoo Jeong, and Ruhi Sarikaya

In this paper, we apply a weakly-supervised learning approach for slot tagging using conditional random fields by exploiting web search click logs. We extend the constrained lattice training of Tackstrom et al. (2013) to ¨ non-linear conditional random fields in which latent variables mediate between observations and labels. When combined with a novel initialization scheme that leverages unlabeled data, we show that our method gives significant improvement over strong supervised and weakly-supervised...

Publication details
Date: 1 June 2015
Type: Proceedings
Publisher: ACL – Association for Computational Linguistics
Sauleh Eetemadi and Kristina Toutanova

Parallel corpora are constructed by taking a document authored in one language and translating it into another language. However, the information about the authored and translated sides of the corpus is usually not preserved. When available, this information can be used to improve statistical machine translation. Existing statistical methods for translation direction detection have low accuracy when applied to the realistic out-of-domain setting, especially when the input texts are short. Our...

Publication details
Date: 1 June 2015
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Publication details
Date: 1 May 2015
Type: Article
Publisher: NAACL
shujie liu, Li Dong, Jiajun Zhang, Furu Wei, Mu Li, and Ming Zhou
Publication details
Date: 1 April 2015
Type: Article
Chen-Tse Tsai, Wen-tau Yih, and Christopher J.C. Burges

Web-based QA, pioneered by Kwok et al. (2001), successfully demonstrated the power of Web redundancy. Early Web-QA systems, such as AskMSR (Brill et al., 2001), rely on various kinds of rewriting and pattern-generation methods for identifying answer paragraphs and for extracting answers. In this paper, we conducted an experimental study to examine the impact of the advance of search engine technologies and the growth of the Web, to such Web-QA approaches. When applying AskMSR to a new question answering...

Publication details
Date: 1 April 2015
Type: Technical report
Number: MSR-TR-2015-20
Shujie Liu, Li Dong, Jiajun Zhang, Furu Wei, Mu Li, and Ming Zhou

随着深度学习逐渐在语音和图像领域获得突破,基于深度学习的机器学习方法在自然语言处理方面的研究也越来越多。本文简要的介绍了深度学习的基本概念和方法,进而着重的介绍深度学习方法是如何被应用到不同类型的自然语言处理任务中,包括词汇化的向量表示,语言模型(序列学习),句法分析(树结构学习),另外还重点介绍了深度学习在自然语言处理的两个关键任务中的应用,即机器翻译和情感分析。

Publication details
Date: 1 April 2015
Type: Article
Grégoire Mesnil, Yann Dauphin, Kaisheng Yao, Yoshua Bengio, Li Deng, Dilek Hakkani-Tur, Xiaodong He, Larry Heck, Gokhan Tur, Dong Yu, and Geoffrey Zweig

Semantic slot filling is one of the most challenging problems in spoken language understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for this task, and present several novel architectures designed to efficiently model past and future temporal dependencies. Specifically, we implemented and compared several important RNN architectures, including Elman, Jordan, and hybrid variants. To facilitate reproducibility, we implemented these networks with the publicly available...

Publication details
Date: 1 March 2015
Type: Article
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Lucy Vanderwende

In this techreport, we provide an introduction to the NLPwin system, a NLP system under development at Microsoft Research. We describe the development methodology, the linguistic representations captured by NLPwin, and we also discuss some of the design decisions that were made in the NLPwin project. A full bibliography is included that covers the papers written about NLPwin as well as the papers written that make use of the NLPwin system output.

Publication details
Date: 1 March 2015
Type: Technical report
Publisher: Microsoft Research
Number: MSR-TR-2015-23
Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward

This paper develops a model that addresses sentence embedding using recurrent neural networks (RNN) with Long Short Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic...

Publication details
Date: 1 February 2015
Type: Article
Publisher: arXiv
Publication details
Date: 4 January 2015
Type: Inproceeding
Spandana Gella, Kalika Bali, and Monojit Choudhury

Language identification is a necessary prerequisite for processing any user generated text, where the language is unknown. It becomes even more challenging when the text is code-mixed, i.e., two or more languages are used within the same text. Such data is commonly seen in social media, where further challenges might arise due to contractions and transliterations. The existing language identification systems are not designed to deal with codemixed text, and as our experiments show, perform poorly on a...

Publication details
Date: 1 December 2014
Type: Inproceeding
Publisher: NLPAI
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