Dominican Republic

Taxonomies of writing systems since Gelb (1952) have classified systems based on what the written symbols represent: if they represent words or morphemes, they are logographic; if syllables, syllabic; if segments, alphabetic; etc. Sproat (2000) and Rogers (2005) broke with tradition by splitting the logographic and phonographic aspects into two dimensions, with logography being graded rather than a categorical distinction. A system could be syllabic, and highly logographic; or alphabetic, and mostly non-logographic. This accords better with how writing systems actually work, but neither author proposed a method for measuring logography.
In this paper we propose a novel measure of the degree of logography that uses an attention- based sequence-to-sequence model trained to predict the spelling of a token from its pronun- ciation in context. In an ideal phonographic system, the model should need to attend to only the current token in order to compute how to spell it, and this would show in the attention matrix activations. In contrast, with a logographic system, where a given pronunciation might correspond to several different spellings, the model would need to attend to a broader context. The ratio of the activation outside the token and the total activation forms the basis of our measure. We compare this with a simple lexical measure, and an entropic measure, as well as several other neural models, and argue that on balance our attention-based measure accords best with intuition about how logographic various systems are.
Our work provides the first quantifiable measure of the notion of logography that accords with linguistic intuition and, we argue, provides better insight into what this notion means.

EMNLP 2021

The Taxonomy of Writing Systems: How to Measure how Logographic a System is

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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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.

technical paper

Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases. To accommodate such requirements, we introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data. Instead of asking for new fine-grained human annotations, we opt to leverage label surface names as the only human guidance and weave in rich pre-trained generative language models into the iterative weak supervision strategy. Specifically, we first propose a label-conditioned finetuning formulation to attune these generators for our task. Furthermore, we devise a regularization objective based on the coarse-fine label constraints derived from our problem setting, giving us even further improvements over the prior formulation. Our framework uses the fine-tuned generative models to sample pseudo-training data for training the classifier, and bootstraps on real unlabeled data for model refinement. Extensive experiments and case studies on two real-world datasets demonstrate superior performance over SOTA zero-shot classification baselines.

Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

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Frequency Effects on Syntactic Rule Learning in Transformers

While there exist scores of natural languages, each with its unique features and idiosyncrasies, they all share a unifying theme: enabling human communication. We may thus reasonably predict that human cognition shapes how these languages evolve and are used. Assuming that the capacity to process information is roughly constant across human populations, we expect a surprisal--duration trade-off to arise both across and within languages. We analyse this trade-off using a corpus of 600 languages and, after controlling for several potential confounds, we find strong supporting evidence in both settings. Specifically, we find that, on average, phones are produced faster in languages where they are less surprising, and vice versa. Further, we confirm that more surprising phones are longer, on average, in 319 languages out of the 600. We thus conclude that there is strong evidence of a surprisal--duration trade-off in operation, both across and within the world's languages.

A surprisal--duration trade-off across and within the world's languages

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Revisiting the Uniform Information Density Hypothesis

We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders. We detail the characteristics of the languages to help researchers understand the challenges that these languages pose for NER. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. We release the data, code, and models in order to inspire future research on African NLP.

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Condenser: a Pre-training Architecture for Dense Retrieval

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We propose a novel interpretable representation that visually grounds dialog history: the Region under Discussion. It constrains the image's spatial features according to a semantic representation of the history inspired by the information structure notion of Question under Discussion. We evaluate the architecture on task-specific multimodal models and the visual transformer model LXMERT and show that there is still room for improvement.

An interpretable representation that visually grounds dialog history

With synthetic data generation, the required amount of human-generated training data can be reduced significantly. In this work, we explore the usage of automatic paraphrasing models such as GPT-2 and CVAE to directly augment the template carrier phrases for task-oriented dialogue systems while preserving the slots, and we systematically analyze how far manually annotated training data can be reduced. We extrinsically evaluate the performance of a natural language understanding systems on augmented data on various levels of data availability, reducing manually written templates up to 75 percent while preserving the accuracy. We further point out that the typical NLG quality metrics such as BLEU, utterance similarity, or utterance perplexity, are not suitable to assess the intrinsic quality of NLU paraphrases, and that the public task-oriented NLU datasets such as ATIS and SNIPS have severe limitations.

Automated Template Paraphrasing for Conversational Assistants

With tightened restrictions in hospital settings, COVID-19 has greatly impacted expecting parents, newborns, and their families. However, no existing work has used natural language processing techniques to analyze impacts of COVID-19 from long-form text written by new parents explicitly describing their birthing experiences. This work investigates whether, and if so how, birthing narratives written by new parents on Reddit changed during COVID-19. To this aim, 4,484 Reddit posts about birthing experiences were analyzed using computational methods including persona analysis and LDA topic modeling. The results of this research indicate that, compared to their frequencies before COVID-19, the presence of partners significantly increased and that of other family members significantly decreased in the narratives during COVID-19. In addition, themes related to induced labor significantly increased in the narratives during COVID-19. This work presents a case study in how patient experience can be analyzed from their own written narratives and perspectives.

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We explore transfer learning to improve optical character recognition (OCR) post-correction, specifically for endangered language texts. We extend an existing OCR post-correction model (Rijhwani et al., 2020) by introducing an additional pretraining step on related data, such as text in a related language or available target endangered language datasets that may differ in orthography. Although cross-lingual transfer is often successful in high-resource settings, our preliminary results show that transferring from related language data decreases performance for this task. On the other hand, we observe small improvements in performance when transferring from additional target language data.

The Taxonomy of Writing Systems: How to Measure how Logographic a System is

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Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

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Revisiting the Uniform Information Density Hypothesis