no code implementations • ACL (dialdoc) 2021 • Hyundong Cho, Genevieve Bartlett, Marjorie Freedman
In this work, we draw parallels between automatically responding to emails for combating social-engineering attacks and document-grounded response generation and lay out the blueprint of our approach.
no code implementations • AKBC Workshop CSKB 2021 • Manuel Ciosici, Joe Cecil, Dong-Ho Lee, Alex Hedges, Marjorie Freedman, Ralph Weischedel
Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e. g., an introductory college textbook or a manual.
1 code implementation • 2 Nov 2023 • Te-Lin Wu, Zi-Yi Dou, Qingyuan Hu, Yu Hou, Nischal Reddy Chandra, Marjorie Freedman, Ralph M. Weischedel, Nanyun Peng
Multimodal counterfactual reasoning is a vital yet challenging ability for AI systems.
no code implementations • 30 Oct 2023 • Manuel R. Ciosici, Alex Hedges, Yash Kankanampati, Justin Martin, Marjorie Freedman, Ralph Weischedel
In work contemporaneous with ours, Lin et al. (2023) demonstrated a two-part approach (SwiftSage) that uses a small LLM (T5-large) complemented by OpenAI's massive LLMs to achieve outstanding results in ScienceWorld.
1 code implementation • 12 Jun 2023 • Shuai Liu, Hyundong J. Cho, Marjorie Freedman, Xuezhe Ma, Jonathan May
Endowing chatbots with a consistent persona is essential to an engaging conversation, yet it remains an unresolved challenge.
no code implementations • ACL 2022 • Te-Lin Wu, Alex Spangher, Pegah Alipoormolabashi, Marjorie Freedman, Ralph Weischedel, Nanyun Peng
The ability to sequence unordered events is an essential skill to comprehend and reason about real world task procedures, which often requires thorough understanding of temporal common sense and multimodal information, as these procedures are often communicated through a combination of texts and images.
no code implementations • 4 Oct 2021 • Manuel R. Ciosici, Joe Cecil, Alex Hedges, Dong-Ho Lee, Marjorie Freedman, Ralph Weischedel
Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e. g., an introductory college textbook or a manual.
1 code implementation • 7 May 2021 • Deniz Beser, Joe Cecil, Marjorie Freedman, Jacob Lichtefeld, Mitch Marcus, Sarah Payne, Charles Yang
We introduce and implement a cognitively plausible model for learning from generic language, statements that express generalizations about members of a category and are an important aspect of concept development in language acquisition (Carlson & Pelletier, 1995; Gelman, 2009).
1 code implementation • 5 May 2021 • Ryan Gabbard, Deniz Beser, Jacob Lichtefeld, Joe Cecil, Mitch Marcus, Sarah Payne, Charles Yang, Marjorie Freedman
We present ADAM, a software system for designing and running child language learning experiments in Python.
1 code implementation • NAACL 2021 • Manuel R. Ciosici, Joseph Cummings, Mitchell DeHaven, Alex Hedges, Yash Kankanampati, Dong-Ho Lee, Ralph Weischedel, Marjorie Freedman
We describe Machine-Aided Script Curator (MASC), a system for human-machine collaborative script authoring.
no code implementations • ACL 2020 • Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman
We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology.
no code implementations • LREC 2020 • Joel Barry, Elizabeth Boschee, Marjorie Freedman, Scott Miller
We describe an approach to cross lingual information retrieval that does not rely on explicit translation of either document or query terms.
no code implementations • 11 Feb 2020 • Tongtao Zhang, Heng Ji, Shih-Fu Chang, Marjorie Freedman
In this paper, we address a practical scenario where training data is released in a sequence of small-scale batches and annotation in earlier phases has lower quality than the later counterparts.
no code implementations • CONLL 2019 • Meryem M{'}hamdi, Marjorie Freedman, Jonathan May
Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.
no code implementations • ACL 2019 • Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, Scott Miller
In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed.
no code implementations • 14 Sep 2018 • Ryan Gabbard, Jay DeYoung, Marjorie Freedman
We explore a human-driven approach to annotation, curated training (CT), in which annotation is framed as teaching the system by using interactive search to identify informative snippets of text to annotate, unlike traditional approaches which either annotate preselected text or use active learning.
no code implementations • IJCNLP 2017 • Bonan Min, Zhuolin Jiang, Marjorie Freedman, Ralph Weischedel
The learnt representation is discriminative and transferable between languages.
no code implementations • EACL 2017 • Bonan Min, Marjorie Freedman, Talya Meltzer
Building knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search.