Nedim Lipka


2021

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Edge: Enriching Knowledge Graph Embeddings with External Text
Saed Rezayi | Handong Zhao | Sungchul Kim | Ryan Rossi | Nedim Lipka | Sheng Li
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on “hard” co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve “soft” augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.

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Syntopical Graphs for Computational Argumentation Tasks
Joe Barrow | Rajiv Jain | Nedim Lipka | Franck Dernoncourt | Vlad Morariu | Varun Manjunatha | Douglas Oard | Philip Resnik | Henning Wachsmuth
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection. We introduce a general approach to these tasks motivated by syntopical reading, a reading process that emphasizes comparing and contrasting viewpoints in order to improve topic understanding. To capture collection-level context, we introduce the syntopical graph, a data structure for linking claims within a collection. A syntopical graph is a typed multi-graph where nodes represent claims and edges represent different possible pairwise relationships, such as entailment, paraphrase, or support. Experiments applying syntopical graphs to the problems of detecting stance and aspects demonstrate state-of-the-art performance in each domain, significantly outperforming approaches that do not utilize collection-level information.

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Learning Contextualized Knowledge Structures for Commonsense Reasoning
Jun Yan | Mrigank Raman | Aaron Chan | Tianyu Zhang | Ryan Rossi | Handong Zhao | Sungchul Kim | Nedim Lipka | Xiang Ren
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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PSED: A Dataset for Selecting Emphasis in Presentation Slides
Amirreza Shirani | Giai Tran | Hieu Trinh | Franck Dernoncourt | Nedim Lipka | Jose Echevarria | Thamar Solorio | Paul Asente
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Let Me Choose: From Verbal Context to Font Selection
Amirreza Shirani | Franck Dernoncourt | Jose Echevarria | Paul Asente | Nedim Lipka | Thamar Solorio
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to. Compared to related work leveraging the surrounding visual context, we choose to focus only on the input text, which can enable new applications for which the text is the only visual element in the document. We introduce a new dataset, containing examples of different topics in social media posts and ads, labeled through crowd-sourcing. Due to the subjective nature of the task, multiple fonts might be perceived as acceptable for an input text, which makes this problem challenging. To this end, we investigate different end-to-end models to learn label distributions on crowd-sourced data, to capture inter-subjectivity across all annotations.

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SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media
Amirreza Shirani | Franck Dernoncourt | Nedim Lipka | Paul Asente | Jose Echevarria | Thamar Solorio
Proceedings of the Fourteenth Workshop on Semantic Evaluation

In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media. The goal of this shared task is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in textual content to enable automated design assistance in authoring. The main focus is on short text instances for social media, with a variety of examples, from social media posts to inspirational quotes. Participants were asked to model emphasis using plain text with no additional context from the user or other design considerations. SemEval-2020 Emphasis Selection shared task attracted 197 participants in the early phase and a total of 31 teams made submissions to this task. The highest-ranked submission achieved 0.823 Matchm score. The analysis of systems submitted to the task indicates that BERT and RoBERTa were the most common choice of pre-trained models used, and part of speech tag (POS) was the most useful feature. Full results can be found on the task’s website.

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ISA: An Intelligent Shopping Assistant
Tuan Lai | Trung Bui | Nedim Lipka
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations

Despite the growth of e-commerce, brick-and-mortar stores are still the preferred destinations for many people. In this paper, we present ISA, a mobile-based intelligent shopping assistant that is designed to improve shopping experience in physical stores. ISA assists users by leveraging advanced techniques in computer vision, speech processing, and natural language processing. An in-store user only needs to take a picture or scan the barcode of the product of interest, and then the user can talk to the assistant about the product. The assistant can also guide the user through the purchase process or recommend other similar products to the user. We take a data-driven approach in building the engines of ISA’s natural language processing component, and the engines achieve good performance.

2019

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Towards Summarization for Social Media - Results of the TL;DR Challenge
Shahbaz Syed | Michael Völske | Nedim Lipka | Benno Stein | Hinrich Schütze | Martin Potthast
Proceedings of the 12th International Conference on Natural Language Generation

In this paper, we report on the results of the TL;DR challenge, discussing an extensive manual evaluation of the expected properties of a good summary based on analyzing the comments provided by human annotators.

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Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions
Amirreza Shirani | Franck Dernoncourt | Paul Asente | Nedim Lipka | Seokhwan Kim | Jose Echevarria | Thamar Solorio
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In visual communication, text emphasis is used to increase the comprehension of written text to convey the author’s intent. We study the problem of emphasis selection, i.e. choosing candidates for emphasis in short written text, to enable automated design assistance in authoring. Without knowing the author’s intent and only considering the input text, multiple emphasis selections are valid. We propose a model that employs end-to-end label distribution learning (LDL) on crowd-sourced data and predicts a selection distribution, capturing the inter-subjectivity (common-sense) in the audience as well as the ambiguity of the input. We compare the model with several baselines in which the problem is transformed to single-label learning by mapping label distributions to absolute labels via majority voting.

2018

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A Simple End-to-End Question Answering Model for Product Information
Tuan Lai | Trung Bui | Sheng Li | Nedim Lipka
Proceedings of the First Workshop on Economics and Natural Language Processing

When evaluating a potential product purchase, customers may have many questions in mind. They want to get adequate information to determine whether the product of interest is worth their money. In this paper we present a simple deep learning model for answering questions regarding product facts and specifications. Given a question and a product specification, the model outputs a score indicating their relevance. To train and evaluate our proposed model, we collected a dataset of 7,119 questions that are related to 153 different products. Experimental results demonstrate that –despite its simplicity– the performance of our model is shown to be comparable to a more complex state-of-the-art baseline.

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Task Proposal: The TL;DR Challenge
Shahbaz Syed | Michael Völske | Martin Potthast | Nedim Lipka | Benno Stein | Hinrich Schütze
Proceedings of the 11th International Conference on Natural Language Generation

The TL;DR challenge fosters research in abstractive summarization of informal text, the largest and fastest-growing source of textual data on the web, which has been overlooked by summarization research so far. The challenge owes its name to the frequent practice of social media users to supplement long posts with a “TL;DR”—for “too long; didn’t read”—followed by a short summary as a courtesy to those who would otherwise reply with the exact same abbreviation to indicate they did not care to read a post for its apparent length. Posts featuring TL;DR summaries form an excellent ground truth for summarization, and by tapping into this resource for the first time, we have mined millions of training examples from social media, opening the door to all kinds of generative models.