Ian Tenney


2022

pdf bib
Retrieval-guided Counterfactual Generation for QA
Bhargavi Paranjape | Matthew Lamm | Ian Tenney
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Deep NLP models have been shown to be brittle to input perturbations. Recent work has shown that data augmentation using counterfactuals — i.e. minimally perturbed inputs — can help ameliorate this weakness. We focus on the task of creating counterfactuals for question answering, which presents unique challenges related to world knowledge, semantic diversity, and answerability. To address these challenges, we develop a Retrieve-Generate-Filter(RGF) technique to create counterfactual evaluation and training data with minimal human supervision. Using an open-domain QA framework and question generation model trained on original task data, we create counterfactuals that are fluent, semantically diverse, and automatically labeled. Data augmentation with RGF counterfactuals improves performance on out-of-domain and challenging evaluation sets over and above existing methods, in both the reading comprehension and open-domain QA settings. Moreover, we find that RGF data leads to significant improvements in a model’s robustness to local perturbations.

2020

pdf bib
Do Language Embeddings capture Scales?
Xikun Zhang | Deepak Ramachandran | Ian Tenney | Yanai Elazar | Dan Roth
Findings of the Association for Computational Linguistics: EMNLP 2020

Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.

pdf bib
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
Yada Pruksachatkun | Phil Yeres | Haokun Liu | Jason Phang | Phu Mon Htut | Alex Wang | Ian Tenney | Samuel R. Bowman
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. jiant enables modular and configuration driven experimentation with state-of-the-art models and a broad set of tasks for probing, transfer learning, and multitask training experiments. jiant implements over 50 NLU tasks, including all GLUE and SuperGLUE benchmark tasks. We demonstrate that jiant reproduces published performance on a variety of tasks and models, e.g., RoBERTa and BERT.

pdf bib
What Happens To BERT Embeddings During Fine-tuning?
Amil Merchant | Elahe Rahimtoroghi | Ellie Pavlick | Ian Tenney
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

While much recent work has examined how linguistic information is encoded in pre-trained sentence representations, comparatively little is understood about how these models change when adapted to solve downstream tasks. Using a suite of analysis techniques—supervised probing, unsupervised similarity analysis, and layer-based ablations—we investigate how fine-tuning affects the representations of the BERT model. We find that while fine-tuning necessarily makes some significant changes, there is no catastrophic forgetting of linguistic phenomena. We instead find that fine-tuning is a conservative process that primarily affects the top layers of BERT, albeit with noteworthy variation across tasks. In particular, dependency parsing reconfigures most of the model, whereas SQuAD and MNLI involve much shallower processing. Finally, we also find that fine-tuning has a weaker effect on representations of out-of-domain sentences, suggesting room for improvement in model generalization.

pdf bib
Do Language Embeddings capture Scales?
Xikun Zhang | Deepak Ramachandran | Ian Tenney | Yanai Elazar | Dan Roth
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.

pdf bib
Asking without Telling: Exploring Latent Ontologies in Contextual Representations
Julian Michael | Jan A. Botha | Ian Tenney
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The success of pretrained contextual encoders, such as ELMo and BERT, has brought a great deal of interest in what these models learn: do they, without explicit supervision, learn to encode meaningful notions of linguistic structure? If so, how is this structure encoded? To investigate this, we introduce latent subclass learning (LSL): a modification to classifier-based probing that induces a latent categorization (or ontology) of the probe’s inputs. Without access to fine-grained gold labels, LSL extracts emergent structure from input representations in an interpretable and quantifiable form. In experiments, we find strong evidence of familiar categories, such as a notion of personhood in ELMo, as well as novel ontological distinctions, such as a preference for fine-grained semantic roles on core arguments. Our results provide unique new evidence of emergent structure in pretrained encoders, including departures from existing annotations which are inaccessible to earlier methods.

pdf bib
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models
Ian Tenney | James Wexler | Jasmijn Bastings | Tolga Bolukbasi | Andy Coenen | Sebastian Gehrmann | Ellen Jiang | Mahima Pushkarna | Carey Radebaugh | Emily Reif | Ann Yuan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models. We focus on core questions about model behavior: Why did my model make this prediction? When does it perform poorly? What happens under a controlled change in the input? LIT integrates local explanations, aggregate analysis, and counterfactual generation into a streamlined, browser-based interface to enable rapid exploration and error analysis. We include case studies for a diverse set of workflows, including exploring counterfactuals for sentiment analysis, measuring gender bias in coreference systems, and exploring local behavior in text generation. LIT supports a wide range of models—including classification, seq2seq, and structured prediction—and is highly extensible through a declarative, framework-agnostic API. LIT is under active development, with code and full documentation available at https://github.com/pair-code/lit.

2019

pdf bib
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Najoung Kim | Roma Patel | Adam Poliak | Patrick Xia | Alex Wang | Tom McCoy | Ian Tenney | Alexis Ross | Tal Linzen | Benjamin Van Durme | Samuel R. Bowman | Ellie Pavlick
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG—our most syntactic objective—performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation.

pdf bib
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Alex Wang | Jan Hula | Patrick Xia | Raghavendra Pappagari | R. Thomas McCoy | Roma Patel | Najoung Kim | Ian Tenney | Yinghui Huang | Katherin Yu | Shuning Jin | Berlin Chen | Benjamin Van Durme | Edouard Grave | Ellie Pavlick | Samuel R. Bowman
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo’s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research.

pdf bib
BERT Rediscovers the Classical NLP Pipeline
Ian Tenney | Dipanjan Das | Ellie Pavlick
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We find that the model represents the steps of the traditional NLP pipeline in an interpretable and localizable way, and that the regions responsible for each step appear in the expected sequence: POS tagging, parsing, NER, semantic roles, then coreference. Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically, revising lower-level decisions on the basis of disambiguating information from higher-level representations.

2018

pdf bib
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse
Manaal Faruqui | Ellie Pavlick | Ian Tenney | Dipanjan Das
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning.