Kangwook Lee


2022

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Learning to Embed Multi-Modal Contexts for Situated Conversational Agents
Haeju Lee | Oh Joon Kwon | Yunseon Choi | Minho Park | Ran Han | Yoonhyung Kim | Jinhyeon Kim | Youngjune Lee | Haebin Shin | Kangwook Lee | Kee-Eung Kim
Findings of the Association for Computational Linguistics: NAACL 2022

The Situated Interactive Multi-Modal Conversations (SIMMC) 2.0 aims to create virtual shopping assistants that can accept complex multi-modal inputs, i.e. visual appearances of objects and user utterances. It consists of four subtasks, multi-modal disambiguation (MM-Disamb), multi-modal coreference resolution (MM-Coref), multi-modal dialog state tracking (MM-DST), and response retrieval and generation. While many task-oriented dialog systems usually tackle each subtask separately, we propose a jointly learned multi-modal encoder-decoder that incorporates visual inputs and performs all four subtasks at once for efficiency. This approach won the MM-Coref and response retrieval subtasks and nominated runner-up for the remaining subtasks using a single unified model at the 10th Dialog Systems Technology Challenge (DSTC10), setting a high bar for the novel task of multi-modal task-oriented dialog systems.

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Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment
Tuan Dinh | Jy-yong Sohn | Shashank Rajput | Timothy Ossowski | Yifei Ming | Junjie Hu | Dimitris Papailiopoulos | Kangwook Lee
Findings of the Association for Computational Linguistics: EMNLP 2022

Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods. Recent findings have shown the improvement in accuracy and robustness of unsupervised word translation (UWT) by utilizing visual observations, which are universal representations across languages.Our work investigates the potential of using not only visual observations but also pretrained language-image models for enabling a more efficient and robust UWT. We develop a novel UWT method dubbed Word Alignment using Language-Image Pretraining (WALIP), leveraging visual observations via the shared image-text embedding space of CLIPs (Radford et al., 2021). WALIP has a two-step procedure. First, we retrieve word pairs with high confidences of similarity, computed using our proposed image-based fingerprints, which define the initial pivot for the alignment.Second, we apply our robust Procrustes algorithm to estimate the linear mapping between two embedding spaces, which iteratively corrects and refines the estimated alignment.Our extensive experiments show that WALIP improves upon the state-of-the-art performance of bilingual word alignment for a few language pairs across different word embeddings and displays great robustness to the dissimilarity of language pairs or training corpora for two word embeddings.

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Debiasing Pre-Trained Language Models via Efficient Fine-Tuning
Michael Gira | Ruisu Zhang | Kangwook Lee
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

An explosion in the popularity of transformer-based language models (such as GPT-3, BERT, RoBERTa, and ALBERT) has opened the doors to new machine learning applications involving language modeling, text generation, and more. However, recent scrutiny reveals that these language models contain inherent biases towards certain demographics reflected in their training data. While research has tried mitigating this problem, existing approaches either fail to remove the bias completely, degrade performance (“catastrophic forgetting”), or are costly to execute. This work examines how to reduce gender bias in a GPT-2 language model by fine-tuning less than 1% of its parameters. Through quantitative benchmarks, we show that this is a viable way to reduce prejudice in pre-trained language models while remaining cost-effective at scale.

2020

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SLM: Learning a Discourse Language Representation with Sentence Unshuffling
Haejun Lee | Drew A. Hudson | Kangwook Lee | Christopher D. Manning
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training methods in NLP focus on learning either bottom or top-level language representations: contextualized word representations derived from language model objectives at one extreme and a whole sequence representation learned by order classification of two given textual segments at the other. However, these models are not directly encouraged to capture representations of intermediate-size structures that exist in natural languages such as sentences and the relationships among them. To that end, we propose a new approach to encourage learning of a contextualized sentence-level representation by shuffling the sequence of input sentences and training a hierarchical transformer model to reconstruct the original ordering. Through experiments on downstream tasks such as GLUE, SQuAD, and DiscoEval, we show that this feature of our model improves the performance of the original BERT by large margins.