Rebecca Knowles

Six Challenges for Neural Machine Translation

We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.

I’m a Belieber: Social Roles via Self-identification and Conceptual Attributes

Motivated by work predicting coarsegrained author categories in social media, such as gender or political preference, we explore whether Twitter contains information to support the prediction of finegrained categories, or social roles. We find that the simple self-identification pattern “I am a ” supports significantly richer classification than previously explored, successfully retrieving a variety of fine-grained roles. For a given role (e.g., writer), we can further identify characteristic attributes using a simple possessive construction (e.g., writer’s ). Tweets that incorporate the attribute terms in first person possessives (my ) are confirmed to be an indicator that the author holds the associated social role.

Creating Interactive Macaronic Interfaces for Language Learning

We present a prototype of a novel technology for second language instruction. Our learn-by-reading approach lets a human learner acquire new words and constructions by encountering them in context. To facilitate reading comprehension, our technology presents mixed native language (L1) and second language (L2) sentences to a learner and allows them to interact with the sentences to make the sentences easier (more L1-like) or harder (more L2-like) to read. Eventually, our system should continuously track a learner’s knowledge and learning style by modeling their interactions, including performance on a pop quiz feature. This will allow our system to generate personalized mixed-language texts for learners.

Demographer: Extremely Simple Name Demographics

The lack of demographic information available when conducting passive analysis of social media content can make it difficult to compare results to traditional survey results. We present DEMOGRAPHER,1 a tool that predicts gender from names, using name lists and a classifier with simple character-level features. By relying only on a name, our tool can make predictions even without extensive user-authored content. We compare DEMOGRAPHER to other available tools and discuss differences in performance. In particular, we show that DEMOGRAPHER performs well on Twitter data, making it useful for simple and rapid social media demographic inference.

User Modeling in Language Learning with Macaronic Texts

Foreign language learners can acquire new vocabulary by using cognate and context clues when reading. To measure such incidental comprehension, we devise an experimental framework that involves reading mixed-language “macaronic” sentences. Using data collected via Amazon Mechanical Turk, we train a graphical model to simulate a human subject’s comprehension of foreign words, based on cognate clues (edit distance to an English word), context clues (pointwise mutual information), and prior exposure. Our model does a reasonable job at predicting which words a user will be able to understand, which should facilitate the automatic construction of comprehensible text for personalized foreign language education.

Analyzing Learner Understanding of Novel L2 Vocabulary

In this work, we explore how learners can infer second-language noun meanings in the context of their native language. Motivated by an interest in building interactive tools for language learning, we collect data on three word-guessing tasks, analyze their difficulty, and explore the types of errors that novice learners make. We train a log-linear model for predicting our subjects’ guesses of word meanings in varying kinds of contexts. The model’s predictions correlate well with subject performance, and we provide quantitative and qualitative analyses of both human and model performance.