Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings. However, orthogonal mapping only works on language pairs whose embeddings are naturally isomorphic. For non-isomorphic pairs, our method (Iterative Normalization) transforms monolingual embeddings to make orthogonal alignment easier by simultaneously enforcing that (1) individual word vectors are unit length, and (2) each language's average vector is zero. Iterative Normalization consistently improves word translation accuracy of three CLWE methods, with the largest improvement observed on English-Japanese (from 2% to 44% test accuracy).
To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments. These approaches extend previously proposed frameworks, including constraints and informed prior-based methods. Users should have a sense of control in HLTM systems, so we propose a control metric to measure whether refinement operations' results match users' expectations. Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics.
Topic models are typically evaluated with respect to the global topic distributions that they generate, using metrics such as coherence, but without regard to local (token-level) topic assignments. Token-level assignments are important for downstream tasks such as classification. Even recent models, which aim to improve the quality of these token-level topic assignments, have been evaluated only with respect to global metrics. We propose a task designed to elicit human judgments of token-level topic assignments. We use a variety of topic model types and parameters and discover that global metrics agree poorly with human assignments. Since human evaluation is expensive we propose a variety of automated metrics to evaluate topic models at a local level. Finally, we correlate our proposed metrics with human judgments from the task on several datasets. We show that an evaluation based on the percent of topic switches correlates most strongly with human judgment of local topic quality. We suggest that this new metric, which we call consistency, be adopted alongside global metrics such as topic coherence when evaluating new topic models.
Recent work establishes dataset difficulty and removes annotation artifacts via partial-input baselines (e.g., hypothesis-only or image-only models). While the success of a partial-input baseline indicates a dataset is cheatable, our work cautions the converse is not necessarily true. Using artificial datasets, we illustrate how the failure of a partial-input baseline might shadow more trivial patterns that are only visible in the full input. We also identify such artifacts in real natural language inference datasets. Our work provides an alternative view on the use of partial-input baselines in future dataset creation.
Quizbowl is a scholastic trivia competition that tests human knowledge and intelligence; additionally, it supports diverse research in question answering (QA). A Quizbowl question consists of multiple sentences whose clues are arranged by difficulty (from obscure to obvious) and uniquely identify a well-known entity such as those found on Wikipedia. Since players can answer the question at any time, an elite player (human or machine) demonstrates its superiority by answering correctly given as few clues as possible. We make two key contributions to machine learning research through Quizbowl: (1) collecting and curating a large factoid QA dataset and an accompanying gameplay dataset, and (2) developing a computational approach to playing Quizbowl that involves determining both what to answer and when to answer. Our Quizbowl system has defeated some of the best trivia players in the world over a multi-year series of exhibition matches. Throughout this paper, we show that collaborations with the vibrant Quizbowl community have contributed to the high quality of our dataset, led to new research directions, and doubled as an exciting way to engage the public with research in machine learning and natural language processing.
Text classification must sometimes be applied in situations with no training data in a target language. However, training data may be available in a related language. We introduce a cross-lingual document classification framework (CACO) between related language pairs. To best use limited training data, our transfer learning scheme exploits cross-lingual subword similarity by jointly training a character-based embedder and a word-based classifier. The embedder derives vector representations for input words from their written forms, and the classifier makes predictions based on the word vectors. We use a joint character representation for both the source language and the target language, which allows the embedder to generalize knowledge about source language words to target language words with similar forms. We propose a multi-task objective that can further improve the model if additional cross-lingual or monolingual resources are available. CACO models trained under low-resource settings rival cross-lingual word embedding models trained under high-resource settings on related language pairs.
Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models. We design a task-specific evaluation for a question answering task and evaluate how well a model interpretation improves human performance in a human-machine cooperative setting. We evaluate interpretation methods in a grounded, realistic setting: playing a trivia game as a team. We also provide design guidance for natural language processing human-in-the-loop settings.
Local model interpretation methods explain individual predictions by assigning an importance value to each input feature. This value is often determined by measuring the change in confidence when a feature is removed. However, the confidence of neural networks is not a robust measure of model uncertainty. This issue makes reliably judging the importance of the input features difficult. We address this by changing the test-time behavior of neural networks using Deep k-Nearest Neighbors. Without harming text classification accuracy, this algorithm provides a more robust uncertainty metric which we use to generate feature importance values. The resulting interpretations better align with human perception than baseline methods. Finally, we use our interpretation method to analyze model predictions on dataset annotation artifacts.
Modern natural language processing systems have been touted as approaching human performance. However, existing datasets are imperfect tests. Examples are written with humans in mind, not computers, and often do not properly expose model limitations. We address this by developing a new process for crowdsourced annotation, adversarial writing, where humans interact with trained models and try to break them. Applying this annotation process to Trivia question answering yields a challenge set, which despite being easy for human players to answer, systematically stumps automated question answering systems. Diagnosing model errors on the evaluation data provides actionable insights to explore in developing more robust and generalizable question answering systems.