Francine Chen

Also published as: Francine R. Chen


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Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data
Sanjeev Kumar Karn | Francine Chen | Yan-Ying Chen | Ulli Waltinger | Hinrich Schütze
Proceedings of the Second Workshop on Domain Adaptation for NLP

Interleaved texts, where posts belonging to different threads occur in a sequence, commonly occur in online chat posts, so that it can be time-consuming to quickly obtain an overview of the discussions. Existing systems first disentangle the posts by threads and then extract summaries from those threads. A major issue with such systems is error propagation from the disentanglement component. While end-to-end trainable summarization system could obviate explicit disentanglement, such systems require a large amount of labeled data. To address this, we propose to pretrain an end-to-end trainable hierarchical encoder-decoder system using synthetic interleaved texts. We show that by fine-tuning on a real-world meeting dataset (AMI), such a system out-performs a traditional two-step system by 22%. We also compare against transformer models and observed that pretraining with synthetic data both the encoder and decoder outperforms the BertSumExtAbs transformer model which pretrains only the encoder on a large dataset.


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Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation
Francine Chen | Yan-Ying Chen
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

A common issue in training a deep learning, abstractive summarization model is lack of a large set of training summaries. This paper examines techniques for adapting from a labeled source domain to an unlabeled target domain in the context of an encoder-decoder model for text generation. In addition to adversarial domain adaptation (ADA), we introduce the use of artificial titles and sequential training to capture the grammatical style of the unlabeled target domain. Evaluation on adapting to/from news articles and Stack Exchange posts indicates that the use of these techniques can boost performance for both unsupervised adaptation as well as fine-tuning with limited target data.


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Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations
Ryuji Kano | Yasuhide Miura | Motoki Taniguchi | Yan-Ying Chen | Francine Chen | Tomoko Ohkuma
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In social media, users can vote, share, or bookmark a post they prefer. The number of these actions is regarded as a measure of popularity. However, popularity is not determined solely by content of a post, e.g., a text or an image it contains, but is highly based on its contexts, e.g., timing, and authority. We propose Disjunctive model that computes the contribution of content and context separately. For evaluation, we build a dataset where the informativeness of comments is annotated. We evaluate the results with ranking metrics, and show that our model outperforms the baseline models which directly use popularity as a measure of informativeness.

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Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking
Jyun-Yu Jiang | Francine Chen | Yan-Ying Chen | Wei Wang
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

An enormous amount of conversation occurs online every day, such as on chat platforms where multiple conversations may take place concurrently. Interleaved conversations lead to difficulties in not only following discussions but also retrieving relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations. In this paper, we propose to leverage representation learning for conversation disentanglement. A Siamese hierarchical convolutional neural network (SHCNN), which integrates local and more global representations of a message, is first presented to estimate the conversation-level similarity between closely posted messages. With the estimated similarity scores, our algorithm for conversation identification by similarity ranking (CISIR) then derives conversations based on high-confidence message pairs and pairwise redundancy. Experiments were conducted with four publicly available datasets of conversations from Reddit and IRC channels. The experimental results show that our approach significantly outperforms comparative baselines in both pairwise similarity estimation and conversation disentanglement.


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Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data
Heike Adel | Francine Chen | Yan-Ying Chen
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Users often use social media to share their interest in products. We propose to identify purchase stages from Twitter data following the AIDA model (Awareness, Interest, Desire, Action). In particular, we define the task of classifying the purchase stage of each tweet in a user’s tweet sequence. We introduce RCRNN, a Ranking Convolutional Recurrent Neural Network which computes tweet representations using convolution over word embeddings and models a tweet sequence with gated recurrent units. Also, we consider various methods to cope with the imbalanced label distribution in our data and show that a ranking layer outperforms class weights.


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Corpus for Customer Purchase Behavior Prediction in Social Media
Shigeyuki Sakaki | Francine Chen | Mandy Korpusik | Yan-Ying Chen
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Many people post about their daily life on social media. These posts may include information about the purchase activity of people, and insights useful to companies can be derived from them: e.g. profile information of a user who mentioned something about their product. As a further advanced analysis, we consider extracting users who are likely to buy a product from the set of users who mentioned that the product is attractive. In this paper, we report our methodology for building a corpus for Twitter user purchase behavior prediction. First, we collected Twitter users who posted a want phrase + product name: e.g. “want a Xperia” as candidate want users, and also candidate bought users in the same way. Then, we asked an annotator to judge whether a candidate user actually bought a product. We also annotated whether tweets randomly sampled from want/bought user timelines are relevant or not to purchase. In this annotation, 58% of want user tweets and 35% of bought user tweets were annotated as relevant. Our data indicate that information embedded in timeline tweets can be used to predict purchase behavior of tweeted products.


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Improving Probabilistic Latent Semantic Analysis with Principal Component Analysis
Ayman Farahat | Francine Chen
11th Conference of the European Chapter of the Association for Computational Linguistics


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Feature-Based Segmentation of Narrative Documents
David Kauchak | Francine Chen
Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing


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Multiple Similarity Measures and Source-Pair Information in Story Link Detection
Francine Chen | Ayman Farahat | Thorsten Brants
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004


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Story Link Detection and New Event Detection are Asymmetric
Francine Chen | Ayman Farahat | Thorsten Brants
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

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Optimizing Story Link Detection is not Equivalent to Optimizing New Event Detection
Ayman Farahat | Francine Chen | Thorsten Brants
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics


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Automatic Discovery of Contextual Factors Describing Phonological Variation
Francine R. Chen | Jeff Shrager
Speech and Natural Language: Proceedings of a Workshop Held at Philadelphia, Pennsylvania, February 21-23, 1989

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Contextually-Based Data-Derived Pronunciation Networks for Automatic Speech Recognition
Francine R. Chen
Speech and Natural Language: Proceedings of a Workshop Held at Cape Cod, Massachusetts, October 15-18, 1989