Dominican Republic

Query auto completion (QAC) is the task of predicting a search engine user’s final query from their intermediate, incomplete query. In this paper, we extend QAC to the streaming voice search setting, where automatic speech recognition systems produce intermediate transcripts as users speak. Naïvely applying existing methods fails because the intermediate transcripts often don’t form prefixes or even substrings of the final transcript. To address this issue, we propose to condition QAC approaches on intermediate transcripts to complete voice queries. We evaluate our models on a speech-enabled smart television with reallife voice search traffic, finding that this ASRaware conditioning improves the completion quality. Our best method obtains an 18% relative improvement in mean reciprocal rank over previous methods.

EMNLP 2021

Voice Query Auto Completion

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers.<br><br><br>
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

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EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Measuring event salience is essential in the understanding of stories. This paper takes a recent unsupervised method for salience detection derived from Barthes Cardinal Functions and theories of surprise and applies it to longer narrative forms. We improve the standard transformer language model by incorporating an external knowledgebase (derived from Retrieval Augmented Generation) and adding a memory mechanism to enhance performance on longer works. We use a novel approach to derive salience annotation using chapter-aligned summaries from the Shmoop corpus for classic literary works. Our evaluation against this data demonstrates that our salience detection model improves performance over and above a non-knowledgebase and memory augmented language model, both of which are crucial to this improvement.

Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories

This paper proposes to study a ﬁne-grained semantic novelty detection task, which can be illustrated with the following example. It is normal that a person walks a dog in the park, but if someone says "A man is walking a chicken in the park," it is novel. Given a set of natural language descriptions of normal scenes, we want to identify descriptions of novel scenes. We are not aware of any existing work that solves the problem. Although existing novelty or anomaly detection algorithms are applicable, since they are usually topic-based, they perform poorly on our ﬁne-grained semantic novelty detection task. This paper proposes an effective model (called GAT-MA) to solve the problem and also contributes a new dataset. Experimental evaluation shows that GAT-MA outperforms 11 baselines by large margins.

Semantic Novelty Detection in Natural Language Descriptions

A challenge in designing high-stakes language assessments is calibrating the test item difficulties, either a priori or from limited pilot test data. While prior work has addressed `cold start' estimation of item difficulties without piloting, we devise a multi-task generalized linear model with BERT features to *jump-start* these estimates, rapidly improving their quality with as few as 500 test-takers and a small sample of item exposures (≈6 each) from a large item bank (≈4,000 items). Our joint model provides a principled way to compare test-taker proficiency, item difficulty, and language proficiency frameworks like the Common European Framework of Reference (CEFR). This also enables new item difficulty estimates without piloting them first, which in turn limits item exposure and thus enhances test security. Finally, using operational data from the Duolingo English Test, a high-stakes English proficiency test, we find that difficulty estimates derived using this method correlate strongly with lexico-grammatical features that correlate with reading complexity.

Jump-Starting Item Parameters for Adaptive Language Tests

Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.

Sequential Cross-Document Coreference Resolution

Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfied in real-world applications. This work presents a corpus-based open-domain event type induction method that automatically discovers a set of event types from a given corpus. As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of <predicate sense, object head> pairs. Specifically, our method (1) selects salient predicates and object heads, (2) disambiguates predicate senses using only a verb sense dictionary, and (3) obtains event types by jointly embedding and clustering <predicate sense, object head> pairs in a latent spherical space. Our experiments, on three datasets from different domains, show our method can discover salient and high-quality event types, according to both automatic and human evaluations.

Corpus-based Open-Domain Event Type Induction

Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Optical character recognition (OCR) can be used to produce digitized text, and previous work has demonstrated the utility of neural post-correction methods that improve the results of general-purpose OCR systems on recognition of less-well-resourced languages. However, these methods rely on manually curated post-correction data, which are relatively scarce compared to the non-annotated raw images that need to be digitized. 

In this paper, we present a semi-supervised learning method that makes it possible to utilize these raw images to improve performance, specifically through the use of self-training, a technique where a model is iteratively trained on its own outputs. In addition, to enforce consistency in the recognized vocabulary, we introduce a lexically aware decoding method that augments the neural post-correction model with a count-based language model constructed from the recognized texts, implemented using weighted finite-state automata (WFSA) for efficient and effective decoding.

Results on four endangered languages demonstrate the utility of the proposed method, with relative error reductions of 15-29%, where we find the combination of self-training and lexically-aware decoding essential for achieving consistent improvements

Lexically-Aware Semi-Supervised Learning for OCR Post-Correction

Event time is one of the most important features for event-event temporal relation extraction. However, explicit event time information in text is sparse. For example, only about 20\% of event mentions in TimeBank-Dense have event-time links. In this paper, we propose a joint model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers. We adopt a Stack-Propagation framework to incorporate predicted relative event time for temporal relation classification and keep the differentiability. Our experiments on MATRES dataset show that our model can significantly improve the RoBERTa-based baseline and achieve state-of-the-art performance.

Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction

Current machine translation methods struggle to translate phrases that contain words that appear infrequently in the training data. Low-resource languages in particular are harmed by this because they have such little data that rare words may be seen only once. This issue can be partly addressed using approaches that explicitly incorporate word-level translation lexicons (Akyürek and Andreas, 2021). However, existing approaches for constructing these lexicons require either substantial human annotation or large datasets. We propose a model for few-shot learning of lexical translation rules. Our approach enables neural machine translation models to efficiently learn translations of infrequent words. In experiments on low-resource English to Spanish and English to Tamil translation, our approach yields BLEU improvements on sentences containing words seen only once during training, resulting in increased performance as a whole.

One-Shot Lexicon Learning for Low-Resource Machine Translation

The presentation includes a brief introduction with providing descriptive details about the confusing points contained in the research paper

Sinhala-English Code-mixed and Code-switched Data Classification

Coreference resolution is the task of identifying mentions that refer to the same entity in a given text. In a discourse, a key part of the interpretation process for advanced spoken dialog requires a coreference module to be present in applications. Our goal is to design a dataset that captures coreference in our system and create a model to improve the discourse in Athena, a chit-chat bot that was a finalist in the the Alexa Prize 2021 Social Bot competition. We then compare its performance on a set of pronominal types: it, they, that, she, he, her, him, his, and hers, and how it performs across the topics of Movies, Sports, Television, and Music. Our findings show that our fine-tuned outperforms an out-of-the-box AllenAI model that uses SpanBERT embeddings.

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Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories

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Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories

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Zero-shot Face-based Voice Conversion: Bottleneck-Free Speech Disentanglement in the Real-world Scenario

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