ACL 2014 - Interactive Language Learning, Visualization, and Interfaces Workshop Proceedings

Aug 20, 2026

Upload by Jacob

ACL 2014 Workshop on Interactive Language Learning, Visualization, and Interfaces brings together researchers across statistical NLP, human-computer interaction, and information visualization. The proceedings focus on how active, online, and interactive machine learning can better incorporate human contributions beyond passive annotation. They also emphasize language-based user interfaces and human-centered design, along with visualization and analysis methods for rapidly growing, diverse linguistic corpora. The goal is to pair NLP techniques with interactive systems that support both experts and non-technical users in exploring linguistic data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages93

Reading Time3 h 54 min

AccessFree to download

Introduction

Active, Online, and Interactive Machine Learning

Language-based user interfaces

Text Visualization and Analysis

Organizers

Program Committee

Invited Speakers

Table of Contents

MiTextExplorer: Linked brushing and mutual information for exploratory text data analysis

Interactive Learning of Spatial Knowledge for Text to 3D Scene Generation

Dynamic Wordclouds and Vennclouds for Exploratory Data Analysis

Active Learning with Constrained Topic Model

GLANCE Visualizes Lexical Phenomena for Language Learning

SPIED: Stanford Pattern based Information Extraction and Diagnostics

Interactive Exploration of Asynchronous Conversations: Applying a User-centered Approach to Design a Visual Text Analytic System

ACL 2014 Workshop on Interactive Language Learning, Visualization, and Interfaces Proceedings of the Workshop June 27, 2014 Baltimore, Maryland, USA 􀀍c2014 The Association for Computational Linguistics Order copies of this and other ACL proceedings from: Association for Computational Linguistics (ACL) 209 N. Eighth Street Stroudsburg, PA 18360 USA Tel: +1-570-476-8006 Fax: +1-570-476-0860 [acl@aclweb.org](acl@aclweb.org) ISBN 978-1-941643-15-0 ii Title Sponsor: Idibon Introduction People acquire language through social interaction. Computers learn linguistic models from data, and increasingly, from language-based exchange with people. How do computational linguistic techniques and interactive visualizations work in concert to improve linguistic data processing for humans and computers? How can statistical learning models be best paired with interactive interfaces? How can the increasing quantity of linguistic data be better explored and analyzed? These questions span statistical natural language processing (NLP), human-computer interaction (HCI), and information visualization (Vis), three ﬁelds with natural connections but infrequent meetings. Vis and HCI are niches in NLP; Vis and HCI have not fully utilized the statistical techniques developed in NLP. This workshop aims to assemble an interdisciplinary community that promotes collaboration across these ﬁelds. Three themes deﬁne this ﬁrst workshop: Active, Online, and Interactive Machine Learning Statistical machine learning (ML) has yielded tremendous gains in coverage and robustness for many tasks, but there is a growing sense that additional error reduction might require a fresh look at the human role. Presently, human inputs are often restricted to passive annotation in ML research. However, the ﬁelds of ML and HCI are both developing new techniques—such as active learning, incremental/online learning, and crowdsourcing—that attempt to engage people in novel and productive ways. How do we jointly solve the learning questions that have been the domain of NLP and address research topics in HCI such as managing human workers and increasing the quality of their responses? Language-based user interfaces NLP techniques have entered mainstream use, but the ﬁeld currently focuses more on building and improving systems and less on understanding how users interact with them in real-world environments. User interface (UI) design decisions can affect the perceived or actual performance of a system. For example, while machine translation (MT) quality improved considerably over the last decade, studies found that human translators disliked MT output for reasons unrelated to translation quality. Many existing systems present sentence-level translations in the absence of relevant context, and disrupt rather than contribute to a translator's workﬂow. How do we best integrate learning methods, user behavior understanding, and human-centered design methodology? Text Visualization and Analysis The quantity and diversity of linguistic corpora is swelling. Recent work on visualizing text data annotated with linguistic structures (e.g., syntactic trees, hypergraphs, and sequences) has produced tools that enable exploration of thematic and recurrence patterns in text. Visual representations built on the outputs of word-level models (e.g., sentiment classiﬁers, topic models, and continuous word embedding models) now power exploratory analysis of legal documents, political text, and social media content. Beyond adding analytic value, interactive visualization can also reduce the upfront effort needed to set up, conﬁgure, and learn a tool, as well as promote adoption. How do we pair appropriate NLP techniques and visualizations to assist both expert and non-technical users, who encounter a growing amount of linguistic data in their professional and everyday lives? Organizers Jason Chuang Spence Green Marti Hearst Jeffrey Heer Philipp Koehn Universit

FAQs

What problem does the workshop address across NLP, HCI, and information visualization?

It asks how computational linguistic techniques and interactive visualizations can work together to improve linguistic data processing for both humans and computers.

How does the workshop define its three themes?

The themes are active/online/interactive machine learning, language-based user interfaces, and text visualization and analysis for exploring linguistic corpora.

Why is interactive visualization important for handling linguistic data?

It helps explore thematic and recurrence patterns, can reduce the effort needed to set up and learn tools, and supports adoption by expert and non-technical users.

Aug 20, 2026

Upload by Jacob