Sorry, you need to enable JavaScript to visit this website.

Natural Language Processing

SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS


Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. Some research works trying to modify the sentiment of the output sequences were reported. In this paper, we propose five models to scale or adjust the sentiment of the chatbot response: persona-based model, reinforcement learning, plug and play model, sentiment transformation network and cycleGAN, all based on the conventional seq2seq model.

Paper Details

Authors:
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee
Submitted On:
15 April 2018 - 4:05am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

lee.pdf

(148)

Keywords

Additional Categories

Subscribe

[1] Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee, "SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2887. Accessed: Aug. 19, 2019.
@article{2887-18,
url = {http://sigport.org/2887},
author = {Chih-Wei Lee; Yau-Shian Wang; Tsung-Yuan Hsu; Kuan-Yu Chen; Hung-Yi Lee; Lin-shan Lee },
publisher = {IEEE SigPort},
title = {SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS
AU - Chih-Wei Lee; Yau-Shian Wang; Tsung-Yuan Hsu; Kuan-Yu Chen; Hung-Yi Lee; Lin-shan Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2887
ER -
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee. (2018). SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS. IEEE SigPort. http://sigport.org/2887
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee, 2018. SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS. Available at: http://sigport.org/2887.
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee. (2018). "SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS." Web.
1. Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen, Hung-Yi Lee, Lin-shan Lee. SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2887

Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation


Keyphrases are short phrases that best represent a document content. They can be useful in a variety of applications, including document summarization and retrieval models. In this paper, we introduce the first dataset of keyphrases for an Arabic document collection, obtained by means of crowdsourcing. We experimentally evaluate different crowdsourced answer aggregation strategies and validate their performances against expert annotations to evaluate the quality of our dataset. We report about our experimental results, the dataset features, some lessons learned, and ideas for future

Paper Details

Authors:
Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini
Submitted On:
30 November 2016 - 4:11am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation

(451)

Keywords

Additional Categories

Subscribe

[1] Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini, "Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1321. Accessed: Aug. 19, 2019.
@article{1321-16,
url = {http://sigport.org/1321},
author = {Marco Basaldella; Eddy Maddalena; Stefano Mizzaro; Gianluca Demartini },
publisher = {IEEE SigPort},
title = {Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation},
year = {2016} }
TY - EJOUR
T1 - Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation
AU - Marco Basaldella; Eddy Maddalena; Stefano Mizzaro; Gianluca Demartini
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1321
ER -
Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini. (2016). Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation. IEEE SigPort. http://sigport.org/1321
Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini, 2016. Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation. Available at: http://sigport.org/1321.
Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini. (2016). "Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation." Web.
1. Marco Basaldella, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini. Towards Building a Standard Dataset for Arabic Keyphrase Extraction Evaluation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1321

Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction


Arabic is one of the fastest growing languages on the Web, with an increasing amount of user generated content being published by both native and non-native speakers all over the world. Despite the great linguistic differences between Arabic and western languages such as English, most Arabic keyphrase extraction systems rely on approaches designed for western languages, thus ignoring its rich morphology and syntax. In this paper we present a new approach leveraging the Arabic morphology and syntax to generate a restricted set of meaningful candidates among which keyphrases are selected.

Paper Details

Authors:
Dario De Nart, Dante Degl’Innocenti, Carlo Tasso
Submitted On:
30 November 2016 - 4:12am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction

(461)

Keywords

Additional Categories

Subscribe

[1] Dario De Nart, Dante Degl’Innocenti, Carlo Tasso, "Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1320. Accessed: Aug. 19, 2019.
@article{1320-16,
url = {http://sigport.org/1320},
author = {Dario De Nart; Dante Degl’Innocenti; Carlo Tasso },
publisher = {IEEE SigPort},
title = {Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction},
year = {2016} }
TY - EJOUR
T1 - Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction
AU - Dario De Nart; Dante Degl’Innocenti; Carlo Tasso
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1320
ER -
Dario De Nart, Dante Degl’Innocenti, Carlo Tasso. (2016). Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction. IEEE SigPort. http://sigport.org/1320
Dario De Nart, Dante Degl’Innocenti, Carlo Tasso, 2016. Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction. Available at: http://sigport.org/1320.
Dario De Nart, Dante Degl’Innocenti, Carlo Tasso. (2016). "Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction." Web.
1. Dario De Nart, Dante Degl’Innocenti, Carlo Tasso. Leveraging Arabic Morphology and Syntax for Achieving Better Keyphrase Extraction [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1320

Aicyber’s System for IALP 2016 Shared Task:Character-enhanced Word Vectors and Boosted Neural Networks

Paper Details

Authors:
Steven Du , Xi Zhang
Submitted On:
21 November 2016 - 6:56pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

IALP2016-114-PDF

(326)

Keywords

Additional Categories

Subscribe

[1] Steven Du , Xi Zhang, "Aicyber’s System for IALP 2016 Shared Task:Character-enhanced Word Vectors and Boosted Neural Networks", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1286. Accessed: Aug. 19, 2019.
@article{1286-16,
url = {http://sigport.org/1286},
author = {Steven Du ; Xi Zhang },
publisher = {IEEE SigPort},
title = {Aicyber’s System for IALP 2016 Shared Task:Character-enhanced Word Vectors and Boosted Neural Networks},
year = {2016} }
TY - EJOUR
T1 - Aicyber’s System for IALP 2016 Shared Task:Character-enhanced Word Vectors and Boosted Neural Networks
AU - Steven Du ; Xi Zhang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1286
ER -
Steven Du , Xi Zhang. (2016). Aicyber’s System for IALP 2016 Shared Task:Character-enhanced Word Vectors and Boosted Neural Networks. IEEE SigPort. http://sigport.org/1286
Steven Du , Xi Zhang, 2016. Aicyber’s System for IALP 2016 Shared Task:Character-enhanced Word Vectors and Boosted Neural Networks. Available at: http://sigport.org/1286.
Steven Du , Xi Zhang. (2016). "Aicyber’s System for IALP 2016 Shared Task:Character-enhanced Word Vectors and Boosted Neural Networks." Web.
1. Steven Du , Xi Zhang. Aicyber’s System for IALP 2016 Shared Task:Character-enhanced Word Vectors and Boosted Neural Networks [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1286

The Effect of Shallow Segmentation for English-Tigrinya Statistical Machine Translation


This paper presents initial English-Tigrinya statistical machine translation (SMT) research. Tigrinya is a highly inflected Semitic language spoken in Eritrea and Ethiopia. Translation involving morphologically complex languages is challenged by factors including data sparseness and source-target word alignment. We try to address these problems through morphological segmentation of Tigrinya words. After segmentation the difference in token count dropped significantly from 37.7% to 0.1%. The out-of-vocabulary rate was reduced by 46%.

Paper Details

Authors:
Yemane Tedla and Kazuhide Yamamoto
Submitted On:
21 November 2016 - 8:31am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

IALP-tig.pdf

(350)

Keywords

Additional Categories

Subscribe

[1] Yemane Tedla and Kazuhide Yamamoto, "The Effect of Shallow Segmentation for English-Tigrinya Statistical Machine Translation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1281. Accessed: Aug. 19, 2019.
@article{1281-16,
url = {http://sigport.org/1281},
author = {Yemane Tedla and Kazuhide Yamamoto },
publisher = {IEEE SigPort},
title = {The Effect of Shallow Segmentation for English-Tigrinya Statistical Machine Translation},
year = {2016} }
TY - EJOUR
T1 - The Effect of Shallow Segmentation for English-Tigrinya Statistical Machine Translation
AU - Yemane Tedla and Kazuhide Yamamoto
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1281
ER -
Yemane Tedla and Kazuhide Yamamoto. (2016). The Effect of Shallow Segmentation for English-Tigrinya Statistical Machine Translation. IEEE SigPort. http://sigport.org/1281
Yemane Tedla and Kazuhide Yamamoto, 2016. The Effect of Shallow Segmentation for English-Tigrinya Statistical Machine Translation. Available at: http://sigport.org/1281.
Yemane Tedla and Kazuhide Yamamoto. (2016). "The Effect of Shallow Segmentation for English-Tigrinya Statistical Machine Translation." Web.
1. Yemane Tedla and Kazuhide Yamamoto. The Effect of Shallow Segmentation for English-Tigrinya Statistical Machine Translation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1281

Word Sense Implantation as Orthographical Conversion


We present a word sense disambiguation (WSD) tool of Japanese Hiragana words. Unlike other WSD tasks which output something like “sense #3” as result, our WSD task rewrites the target word into a Kanji word, which is a different orthography. This means that the task is also a kind of orthographical normalization as well as WSD. In this paper we present the task, our method, and the performance.

Paper Details

Authors:
Kazuhide Yamamoto and Yuki Mikami
Submitted On:
21 November 2016 - 8:27am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

IALP-wsd.pdf

(326)

Keywords

Additional Categories

Subscribe

[1] Kazuhide Yamamoto and Yuki Mikami, "Word Sense Implantation as Orthographical Conversion", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1280. Accessed: Aug. 19, 2019.
@article{1280-16,
url = {http://sigport.org/1280},
author = {Kazuhide Yamamoto and Yuki Mikami },
publisher = {IEEE SigPort},
title = {Word Sense Implantation as Orthographical Conversion},
year = {2016} }
TY - EJOUR
T1 - Word Sense Implantation as Orthographical Conversion
AU - Kazuhide Yamamoto and Yuki Mikami
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1280
ER -
Kazuhide Yamamoto and Yuki Mikami. (2016). Word Sense Implantation as Orthographical Conversion. IEEE SigPort. http://sigport.org/1280
Kazuhide Yamamoto and Yuki Mikami, 2016. Word Sense Implantation as Orthographical Conversion. Available at: http://sigport.org/1280.
Kazuhide Yamamoto and Yuki Mikami. (2016). "Word Sense Implantation as Orthographical Conversion." Web.
1. Kazuhide Yamamoto and Yuki Mikami. Word Sense Implantation as Orthographical Conversion [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1280

Japanese Orthographical Normalization Does Not Work for Statistical Machine Translation


We have investigated the effect of normalizing Japanese orthographical variants into a uniform orthography on statistical machine translation (SMT) between Japanese and English. In Japanese, 10% of words have reportedly more than one orthographical variants, which is a promising fact for improving translation quality when we normalize these orthographical variants.

Paper Details

Authors:
Kazuhide Yamamoto, Kanji Takahashi
Submitted On:
21 November 2016 - 8:28pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

15-IALP2016.pdf

(292)

Keywords

Additional Categories

Subscribe

[1] Kazuhide Yamamoto, Kanji Takahashi, "Japanese Orthographical Normalization Does Not Work for Statistical Machine Translation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1273. Accessed: Aug. 19, 2019.
@article{1273-16,
url = {http://sigport.org/1273},
author = {Kazuhide Yamamoto; Kanji Takahashi },
publisher = {IEEE SigPort},
title = {Japanese Orthographical Normalization Does Not Work for Statistical Machine Translation},
year = {2016} }
TY - EJOUR
T1 - Japanese Orthographical Normalization Does Not Work for Statistical Machine Translation
AU - Kazuhide Yamamoto; Kanji Takahashi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1273
ER -
Kazuhide Yamamoto, Kanji Takahashi. (2016). Japanese Orthographical Normalization Does Not Work for Statistical Machine Translation. IEEE SigPort. http://sigport.org/1273
Kazuhide Yamamoto, Kanji Takahashi, 2016. Japanese Orthographical Normalization Does Not Work for Statistical Machine Translation. Available at: http://sigport.org/1273.
Kazuhide Yamamoto, Kanji Takahashi. (2016). "Japanese Orthographical Normalization Does Not Work for Statistical Machine Translation." Web.
1. Kazuhide Yamamoto, Kanji Takahashi. Japanese Orthographical Normalization Does Not Work for Statistical Machine Translation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1273

Fundamental Tools and Resource are Available for Vietnamese Analysis


This paper presents our work on developing Vietnamese fundamental tools and a resource for analysis. These tools are for word segmentation and part-of-speech tagging, diacritics restoration, and orthographical variants dictionary. All of them have been either not publicly available so far or not attaining sufficient performance. We have developed the tools and released the tools to the public, in both software packages and web tools. For development, we utilize state-of-the-art methods and achieved high accuracy.

Paper Details

Authors:
Kanji Takahash, Kazuhide Yamamoto
Submitted On:
21 November 2016 - 4:25am
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

68-IALP2016.pdf

(291)

Keywords

Additional Categories

Subscribe

[1] Kanji Takahash, Kazuhide Yamamoto, "Fundamental Tools and Resource are Available for Vietnamese Analysis", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1271. Accessed: Aug. 19, 2019.
@article{1271-16,
url = {http://sigport.org/1271},
author = {Kanji Takahash; Kazuhide Yamamoto },
publisher = {IEEE SigPort},
title = {Fundamental Tools and Resource are Available for Vietnamese Analysis},
year = {2016} }
TY - EJOUR
T1 - Fundamental Tools and Resource are Available for Vietnamese Analysis
AU - Kanji Takahash; Kazuhide Yamamoto
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1271
ER -
Kanji Takahash, Kazuhide Yamamoto. (2016). Fundamental Tools and Resource are Available for Vietnamese Analysis. IEEE SigPort. http://sigport.org/1271
Kanji Takahash, Kazuhide Yamamoto, 2016. Fundamental Tools and Resource are Available for Vietnamese Analysis. Available at: http://sigport.org/1271.
Kanji Takahash, Kazuhide Yamamoto. (2016). "Fundamental Tools and Resource are Available for Vietnamese Analysis." Web.
1. Kanji Takahash, Kazuhide Yamamoto. Fundamental Tools and Resource are Available for Vietnamese Analysis [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1271

A Regression Approach to Valence-Arousal Ratings of Words from Word

Paper Details

Authors:
Submitted On:
17 November 2016 - 6:54am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

A Regression Approach to Valence-Arousal Ratings of Words from Word-PPT.pdf

(66)

Keywords

Additional Categories

Subscribe

[1] , "A Regression Approach to Valence-Arousal Ratings of Words from Word", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1268. Accessed: Aug. 19, 2019.
@article{1268-16,
url = {http://sigport.org/1268},
author = { },
publisher = {IEEE SigPort},
title = {A Regression Approach to Valence-Arousal Ratings of Words from Word},
year = {2016} }
TY - EJOUR
T1 - A Regression Approach to Valence-Arousal Ratings of Words from Word
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1268
ER -
. (2016). A Regression Approach to Valence-Arousal Ratings of Words from Word. IEEE SigPort. http://sigport.org/1268
, 2016. A Regression Approach to Valence-Arousal Ratings of Words from Word. Available at: http://sigport.org/1268.
. (2016). "A Regression Approach to Valence-Arousal Ratings of Words from Word." Web.
1. . A Regression Approach to Valence-Arousal Ratings of Words from Word [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1268