Song Feng


2021

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MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents
Song Feng | Siva Sankalp Patel | Hui Wan | Sachindra Joshi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as machine reading comprehension task based on a single given document or passage. In this work, we aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents. To facilitate such task, we introduce a new dataset that contains dialogues grounded in multiple documents from four different domains. We also explore modeling the dialogue-based and document-based contexts in the dataset. We present strong baseline approaches and various experimental results, aiming to support further research efforts on such a task.

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Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks
Hanjie Chen | Song Feng | Jatin Ganhotra | Hui Wan | Chulaka Gunasekara | Sachindra Joshi | Yangfeng Ji
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, for models with text pairs as inputs (e.g., paraphrase identification), existing methods are not sufficient to capture feature interactions between two texts and their simple extension of computing all word-pair interactions between two texts is computationally inefficient. In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole. The proposed method is evaluated with two different model architectures (decomposable attention model and BERT) across four datasets, including natural language inference and paraphrase identification tasks. Experiments show the effectiveness of GMASK in providing faithful explanations to these models.

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Does Structure Matter? Encoding Documents for Machine Reading Comprehension
Hui Wan | Song Feng | Chulaka Gunasekara | Siva Sankalp Patel | Sachindra Joshi | Luis Lastras
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Machine reading comprehension is a challenging task especially for querying documents with deep and interconnected contexts. Transformer-based methods have shown advanced performances on this task; however, most of them still treat documents as a flat sequence of tokens. This work proposes a new Transformer-based method that reads a document as tree slices. It contains two modules for identifying more relevant text passage and the best answer span respectively, which are not only jointly trained but also jointly consulted at inference time. Our evaluation results show that our proposed method outperforms several competitive baseline approaches on two datasets from varied domains.

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Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)
Song Feng | Siva Reddy | Malihe Alikhani | He He | Yangfeng Ji | Mohit Iyyer | Zhou Yu
Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)

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DialDoc 2021 Shared Task: Goal-Oriented Document-grounded Dialogue Modeling
Song Feng
Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)

We present the results of Shared Task at Workshop DialDoc 2021 that is focused on document-grounded dialogue and conversational question answering. The primary goal of this Shared Task is to build goal-oriented information-seeking conversation systems that can identify the most relevant knowledge in the associated document for generating agent responses in natural language. It includes two subtasks on predicting agent responses: the first subtask is to predict the grounding text span in the given document for next agent response; the second subtask is to generate agent response in natural language given the context. Many submissions outperform baseline significantly. For the first task, the best-performing system achieved 67.1 Exact Match and 76.3 F1. For the second subtask, the best system achieved 41.1 SacreBLEU and highest rank by human evaluation.

2020

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doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset
Song Feng | Hui Wan | Chulaka Gunasekara | Siva Patel | Sachindra Joshi | Luis Lastras
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents. Inspired by how the authors compose documents for guiding end users, we first construct dialogue flows based on the content elements that corresponds to higher-level relations across text sections as well as lower-level relations between discourse units within a section. Then we present these dialogue flows to crowd contributors to create conversational utterances. The dataset includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets, this dataset covers a variety of dialogue scenes in information-seeking conversations. For evaluating the versatility of the dataset, we introduce multiple dialogue modeling tasks and present baseline approaches.

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Implicit Discourse Relation Classification: We Need to Talk about Evaluation
Najoung Kim | Song Feng | Chulaka Gunasekara | Luis Lastras
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Implicit relation classification on Penn Discourse TreeBank (PDTB) 2.0 is a common benchmark task for evaluating the understanding of discourse relations. However, the lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in the literature. In this work, we highlight these inconsistencies and propose an improved evaluation protocol. Paired with this protocol, we report strong baseline results from pretrained sentence encoders, which set the new state-of-the-art for PDTB 2.0. Furthermore, this work is the first to explore fine-grained relation classification on PDTB 3.0. We expect our work to serve as a point of comparison for future work, and also as an initiative to discuss models of larger context and possible data augmentations for downstream transferability.

2019

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Chat-crowd: A Dialog-based Platform for Visual Layout Composition
Paola Cascante-Bonilla | Xuwang Yin | Vicente Ordonez | Song Feng
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

In this paper we introduce Chat-crowd, an interactive environment for visual layout composition via conversational interactions. Chat-crowd supports multiple agents with two conversational roles: agents who play the role of a designer are in charge of placing objects in an editable canvas according to instructions or commands issued by agents with a director role. The system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection and is equipped with comprehensive quality controls on the performance of both types of agents. We expect that this system will be useful to build multimodal goal-oriented dialog tasks that require spatial and geometric reasoning.

2018

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World Knowledge for Abstract Meaning Representation Parsing
Charles Welch | Jonathan K. Kummerfeld | Song Feng | Rada Mihalcea
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2014

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Keystroke Patterns as Prosody in Digital Writings: A Case Study with Deceptive Reviews and Essays
Ritwik Banerjee | Song Feng | Jun Seok Kang | Yejin Choi
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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ConnotationWordNet: Learning Connotation over the Word+Sense Network
Jun Seok Kang | Song Feng | Leman Akoglu | Yejin Choi
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Connotation Lexicon: A Dash of Sentiment Beneath the Surface Meaning
Song Feng | Jun Seok Kang | Polina Kuznetsova | Yejin Choi
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Success with Style: Using Writing Style to Predict the Success of Novels
Vikas Ganjigunte Ashok | Song Feng | Yejin Choi
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Syntactic Stylometry for Deception Detection
Song Feng | Ritwik Banerjee | Yejin Choi
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Characterizing Stylistic Elements in Syntactic Structure
Song Feng | Ritwik Banerjee | Yejin Choi
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Learning General Connotation of Words using Graph-based Algorithms
Song Feng | Ritwik Bose | Yejin Choi
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing