publications

2022

1. NTULM: Enriching Social Media Text Representations with Non-Textual Units

   Jinning Li, Shubhanshu Mishra, Ahmed El-Kishky, and 2 more authors

   In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), Oct 2022

   Abs

   On social media, additional context is often present in the form of annotations and meta-data such as the post’s author, mentions, Hashtags, and hyperlinks. We refer to these annotations as Non-Textual Units (NTUs). We posit that NTUs provide social context beyond their textual semantics and leveraging these units can enrich social media text representations. In this work we construct an NTU-centric social heterogeneous network to co-embed NTUs. We then principally integrate these NTU embeddings into a large pretrained language model by fine-tuning with these additional units. This adds context to noisy short-text social media. Experiments show that utilizing NTU-augmented text representations significantly outperforms existing text-only baselines by 2-5% relative points on many downstream tasks highlighting the importance of context to social media NLP. We also highlight that including NTU context into the initial layers of language model alongside text is better than using it after the text embedding is generated. Our work leads to the generation of holistic general purpose social media content embedding.

2. TweetNERD - End to End Entity Linking Benchmark for Tweets

   Shubhanshu Mishra, Aman Saini, Raheleh Makki, and 3 more authors

   In Advances in Neural Information Processing Systems, Oct 2022
3. Towards Improved Distantly Supervised Multilingual Named-Entity Recognition for Tweets

   Ramy Eskander, Shubhanshu Mishra, Sneha Mehta, and 2 more authors

   In Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL), Dec 2022

   Abs

   Recent low-resource named-entity recognition (NER) work has shown impressive gains by leveraging a single multilingual model trained using distantly supervised data derived from cross-lingual knowledge bases. In this work, we investigate such approaches by leveraging Wikidata to build large-scale NER datasets of Tweets and propose two orthogonal improvements for low-resource NER in the Twitter social media domain: (1) leveraging domain-specific pre-training on Tweets; and (2) building a model for each language family rather than an all-in-one single multilingual model. For (1), we show that mBERT with Tweet pre-training outperforms the state-of-the-art multilingual transformer-based language model, LaBSE, by a relative increase of 34.6% in F1 when evaluated on Twitter data in a language-agnostic multilingual setting. For (2), we show that learning NER models for language families outperforms a single multilingual model by relative increases of 14.1%, 15.8% and 45.3% in F1 when utilizing mBERT, mBERT with Tweet pre-training and LaBSE, respectively. We conduct analyses and present examples for these observed improvements.

4. Improving Zero-Shot Event Extraction via Sentence Simplification

   Sneha Mehta, Huzefa Rangwala, and Naren Ramakrishnan

   Dec 2022
5. Improving Zero-Shot Event Extraction via Sentence Simplification

   Sneha Mehta, Huzefa Rangwala, and Naren Ramakrishnan

   Dec 2022

2020

1. Low Rank Factorization for Compact Multi-Head Self-Attention

   Sneha Mehta, Huzefa Rangwala, and Naren Ramakrishnan

   Dec 2020
2. Simplify-Then-Translate: Automatic Preprocessing for Black-Box Translation

   Sneha Mehta, Bahareh Azarnoush, Boris Chen, and 4 more authors

   Proceedings of the AAAI Conference on Artificial Intelligence, Apr 2020

2019

1. Photo Sleuth: Combining Human Expertise and Face Recognition to Identify Historical Portraits

   Vikram Mohanty, David Thames, Sneha Mehta, and 1 more author

   In Proceedings of the 24th International Conference on Intelligent User Interfaces, Apr 2019

   Abs

   Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this paper, we focus on identifying portraits of soldiers who participated in the American Civil War (1861-65), the first widely-photographed conflict. Many thousands of these portraits survive, but only 10–20% are identified. We created Photo Sleuth, a web-based platform that combines crowdsourced human expertise and automated face recognition to support Civil War portrait identification. Our mixed-methods evaluation of Photo Sleuth one month after its public launch showed that it helped users successfully identify unknown portraits and provided a sustainable model for volunteer contribution. We also discuss implications for crowd-AI interaction and person identification pipelines.

2. Event Detection Using Hierarchical Multi-Aspect Attention

   Sneha Mehta, Mohammad Raihanul Islam, Huzefa Rangwala, and 1 more author

   In The World Wide Web Conference, Apr 2019

   Abs

   Classical event encoding and extraction methods rely on fixed dictionaries of keywords and templates or require ground truth labels for phrase/sentences. This hinders widespread application of information encoding approaches to large-scale free form (unstructured) text available on the web. Event encoding can be viewed as a hierarchical task where the coarser level task is event detection, i.e., identification of documents containing a specific event, and where the fine-grained task is one of event encoding, i.e., identifying key phrases, key sentences. Hierarchical models with attention seem like a natural choice for this problem, given their ability to differentially attend to more or less important features when constructing document representations. In this work we present a novel factorized bilinear multi-aspect attention mechanism (FBMA) that attends to different aspects of text while constructing its representation. We find that our approach outperforms state-of-the-art baselines for detecting civil unrest, military action, and non-state actor events from corpora in two different languages.