Alex Marin


2022

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Extracting and Inferring Personal Attributes from Dialogue
Zhulin Wang | Xuhui Zhou | Rik Koncel-Kedziorski | Alex Marin | Fei Xia
Proceedings of the 4th Workshop on NLP for Conversational AI

Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes. We introduce the tasks of extracting and inferring personal attributes from human-human dialogue, and analyze the linguistic demands of these tasks. To meet these challenges, we introduce a simple and extensible model that combines an autoregressive language model utilizing constrained attribute generation with a discriminative reranker. Our model outperforms strong baselines on extracting personal attributes as well as inferring personal attributes that are not contained verbatim in utterances and instead requires commonsense reasoning and lexical inferences, which occur frequently in everyday conversation. Finally, we demonstrate the benefit of incorporating personal attributes in social chit-chat and task-oriented dialogue settings.

2021

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The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
Ernie Chang | Xiaoyu Shen | Alex Marin | Vera Demberg
Proceedings of the 14th International Conference on Natural Language Generation

We propose a shared task on training instance selection for few-shot neural text generation. Large-scale pretrained language models have led to dramatic improvements in few-shot text generation. Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. Studying the selection strategy can help us (1) make the most use of our annotation budget in downstream tasks and (2) better benchmark few-shot text generative models. We welcome submissions that present their selection strategies and the effects on the generation quality.

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Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling
Ernie Chang | Vera Demberg | Alex Marin
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive. Recent frameworks address this bottleneck with generative models that synthesize weak labels at scale, where a small amount of training labels are expert-curated and the rest of the data is automatically annotated. We follow that approach, by automatically constructing a large-scale weakly-labeled data with a fine-tuned GPT-2, and employ a semi-supervised framework to jointly train the NLG and NLU models. The proposed framework adapts the parameter updates to the models according to the estimated label-quality. On both the E2E and Weather benchmarks, we show that this weakly supervised training paradigm is an effective approach under low resource scenarios with as little as 10 data instances, and outperforming benchmark systems on both datasets when 100% of the training data is used.

2020

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DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool
Ernie Chang | Jeriah Caplinger | Alex Marin | Xiaoyu Shen | Vera Demberg
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions. The tool is implemented as an interactive application that reduces human efforts in annotating large quantities of structured data, e.g. in the format of a table or tree structure. By using a backend sequence-to-sequence model, our system iteratively analyzes the annotated labels in order to better sample unlabeled data. In a simulation experiment performed on annotating large quantities of structured data, DART has been shown to reduce the total number of annotations needed with active learning and automatically suggesting relevant labels.

2019

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Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-Human Conversation Scenarios
Kunho Kim | Rahul Jha | Kyle Williams | Alex Marin | Imed Zitouni
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Task oriented language understanding (LU) in human-to-machine (H2M) conversations has been extensively studied for personal digital assistants. In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task. Recent advances on LU in H2M conversations have shown accuracy improvements by adding encoded knowledge from different sources. Inspired by this, we explore several variants of a bidirectional LSTM architecture that relies on different knowledge sources, such as Web data, search engine click logs, expert feedback from H2M models, as well as previous utterances in the conversation. We also propose ensemble techniques that aggregate these different knowledge sources into a single model. Experimental evaluation on a four-turn Twitter dataset in the restaurant and music domains shows improvements in the slot tagging F1-score of up to 6.09% compared to existing approaches.

2018

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Bag of Experts Architectures for Model Reuse in Conversational Language Understanding
Rahul Jha | Alex Marin | Suvamsh Shivaprasad | Imed Zitouni
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

Slot tagging, the task of detecting entities in input user utterances, is a key component of natural language understanding systems for personal digital assistants. Since each new domain requires a different set of slots, the annotation costs for labeling data for training slot tagging models increases rapidly as the number of domains grow. To tackle this, we describe Bag of Experts (BoE) architectures for model reuse for both LSTM and CRF based models. Extensive experimentation over a dataset of 10 domains drawn from data relevant to our commercial personal digital assistant shows that our BoE models outperform the baseline models with a statistically significant average margin of 5.06% in absolute F1-score when training with 2000 instances per domain, and achieve an even higher improvement of 12.16% when only 25% of the training data is used.

2016

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Task Completion Platform: A self-serve multi-domain goal oriented dialogue platform
Paul Crook | Alex Marin | Vipul Agarwal | Khushboo Aggarwal | Tasos Anastasakos | Ravi Bikkula | Daniel Boies | Asli Celikyilmaz | Senthilkumar Chandramohan | Zhaleh Feizollahi | Roman Holenstein | Minwoo Jeong | Omar Khan | Young-Bum Kim | Elizabeth Krawczyk | Xiaohu Liu | Danko Panic | Vasiliy Radostev | Nikhil Ramesh | Jean-Phillipe Robichaud | Alexandre Rochette | Logan Stromberg | Ruhi Sarikaya
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

2011

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Detecting Forum Authority Claims in Online Discussions
Alex Marin | Bin Zhang | Mari Ostendorf
Proceedings of the Workshop on Language in Social Media (LSM 2011)

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Annotating Social Acts: Authority Claims and Alignment Moves in Wikipedia Talk Pages
Emily M. Bender | Jonathan T. Morgan | Meghan Oxley | Mark Zachry | Brian Hutchinson | Alex Marin | Bin Zhang | Mari Ostendorf
Proceedings of the Workshop on Language in Social Media (LSM 2011)