Anna Rogers


2021

pdf bib
Changing the World by Changing the Data
Anna Rogers
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

NLP community is currently investing a lot more research and resources into development of deep learning models than training data. While we have made a lot of progress, it is now clear that our models learn all kinds of spurious patterns, social biases, and annotation artifacts. Algorithmic solutions have so far had limited success. An alternative that is being actively discussed is more careful design of datasets so as to deliver specific signals. This position paper maps out the arguments for and against data curation, and argues that fundamentally the point is moot : curation already is and will be happening, and it is changing the world. The question is only how much thought we want to invest into that process.

pdf bib
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)
Anna Rogers | Iacer Calixto | Ivan Vulić | Naomi Saphra | Nora Kassner | Oana-Maria Camburu | Trapit Bansal | Vered Shwartz
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

pdf bib
Proceedings of the Second Workshop on Insights from Negative Results in NLP
João Sedoc | Anna Rogers | Anna Rumshisky | Shabnam Tafreshi
Proceedings of the Second Workshop on Insights from Negative Results in NLP

2020

pdf bib
What Can We Do to Improve Peer Review in NLP?NLP?
Anna Rogers | Isabelle Augenstein
Findings of the Association for Computational Linguistics: EMNLP 2020

Peer review is our best tool for judging the quality of conference submissions, but it is becoming increasingly spurious. We argue that a part of the problem is that the reviewers and area chairs face a poorly defined task forcing apples-to-oranges comparisons. There are several potential ways forward, but the key difficulty is creating the incentives and mechanisms for their consistent implementation in the NLP community.

pdf bib
Proceedings of the First Workshop on Insights from Negative Results in NLP
Anna Rogers | João Sedoc | Anna Rumshisky
Proceedings of the First Workshop on Insights from Negative Results in NLP

2019

pdf bib
Revealing the Dark Secrets of BERTBERT
Olga Kovaleva | Alexey Romanov | Anna Rogers | Anna Rumshisky
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

BERT-based architectures currently give state-of-the-art performance on many NLP tasks, but little is known about the exact mechanisms that contribute to its success. In the current work, we focus on the interpretation of self-attention, which is one of the fundamental underlying components of BERT. Using a subset of GLUE tasks and a set of handcrafted features-of-interest, we propose the methodology and carry out a qualitative and quantitative analysis of the information encoded by the individual BERT’s heads. Our findings suggest that there is a limited set of attention patterns that are repeated across different heads, indicating the overall model overparametrization. While different heads consistently use the same attention patterns, they have varying impact on performance across different tasks. We show that manually disabling attention in certain heads leads to a performance improvement over the regular fine-tuned BERT models.

pdf bib
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP
Anna Rogers | Aleksandr Drozd | Anna Rumshisky | Yoav Goldberg
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP

pdf bib
Adversarial Decomposition of Text Representation
Alexey Romanov | Anna Rumshisky | Anna Rogers | David Donahue
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

In this paper, we present a method for adversarial decomposition of text representation. This method can be used to decompose a representation of an input sentence into several independent vectors, each of them responsible for a specific aspect of the input sentence. We evaluate the proposed method on two case studies : the conversion between different social registers and diachronic language change. We show that the proposed method is capable of fine-grained controlled change of these aspects of the input sentence. It is also learning a continuous (rather than categorical) representation of the style of the sentence, which is more linguistically realistic. The model uses adversarial-motivational training and includes a special motivational loss, which acts opposite to the discriminator and encourages a better decomposition. Furthermore, we evaluate the obtained meaning embeddings on a downstream task of paraphrase detection and show that they significantly outperform the embeddings of a regular autoencoder.

2018

pdf bib
What’s in Your Embedding, And How It Predicts Task Performance
Anna Rogers | Shashwath Hosur Ananthakrishna | Anna Rumshisky
Proceedings of the 27th International Conference on Computational Linguistics

Attempts to find a single technique for general-purpose intrinsic evaluation of word embeddings have so far not been successful. We present a new approach based on scaled-up qualitative analysis of word vector neighborhoods that quantifies interpretable characteristics of a given model (e.g. its preference for synonyms or shared morphological forms as nearest neighbors). We analyze 21 such factors and show how they correlate with performance on 14 extrinsic and intrinsic task datasets (and also explain the lack of correlation between some of them). Our approach enables multi-faceted evaluation, parameter search, and generally a more principled, hypothesis-driven approach to development of distributional semantic representations.

pdf bib
Subcharacter Information in Japanese Embeddings : When Is It Worth It?Japanese Embeddings: When Is It Worth It?
Marzena Karpinska | Bofang Li | Anna Rogers | Aleksandr Drozd
Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP

Languages with logographic writing systems present a difficulty for traditional character-level models. Leveraging the subcharacter information was recently shown to be beneficial for a number of intrinsic and extrinsic tasks in Chinese. We examine whether the same strategies could be applied for Japanese, and contribute a new analogy dataset for this language.

2017

pdf bib
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
Bofang Li | Tao Liu | Zhe Zhao | Buzhou Tang | Aleksandr Drozd | Anna Rogers | Xiaoyong Du
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task.