Papers by Ian Tenney
WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse (D18-1)
Copied to clipboard
| Challenge: | a corpus of 43 million atomic edits is available for Wikipedia edit history . edits are instances in which a human editor has inserted a single contiguous phrase into, or deleted a contigous phrase from, an existing sentence. |
| Approach: | They use Wikipedia edit history to mine atomic edits across 8 languages . they find edits contain instances in which a human editor has inserted a single phrase into, or deleted a contiguous phrase from, an existing sentence. |
| Outcome: | The data show that edits differ from the language observed in standard corpora and that models trained on edits encode different aspects of semantics and discourse than models trained in raw text. |
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models (2020.acl-demos)
Copied to clipboard
Yada Pruksachatkun, Phil Yeres, Haokun Liu, Jason Phang, Phu Mon Htut, Alex Wang, Ian Tenney, Samuel R. Bowman
| Challenge: | jiant is an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. |
| Approach: | They introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. |
| Outcome: | The proposed toolkit reproduces published performance on GLUE and SuperGLUE tasks. |
Asking without Telling: Exploring Latent Ontologies in Contextual Representations (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on model analysis indicates that they may learn a lot about linguistic structure, including part of speech, syntax, word sense, and more. |
| Approach: | They introduce latent subclass learning, a modification to classifier-based probing that induces a latent categorization (or ontology) of the probe’s inputs. |
| Outcome: | The proposed model induces a latent categorization (or ontology) of the probe’s inputs without access to fine-grained gold labels. |
BERT Rediscovers the Classical NLP Pipeline (P19-1)
Copied to clipboard
| Challenge: | Pre-trained text encoders have advanced the state of the art on many NLP tasks . Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically . |
| Approach: | They aim to quantify where linguistic information is captured within a network model . they aim to use pre-trained text encoders to displace static word embeddings . |
| Outcome: | The proposed model can adjust the pipeline dynamically, revealing lower-level decisions on the basis of disambiguation from higher-level representations. |
Retrieval-guided Counterfactual Generation for QA (2022.acl-long)
Copied to clipboard
| Challenge: | Recent work shows that data augmentation using counterfactuals can help ameliorate this weakness. |
| Approach: | They propose a Retrieve-Generate-Filter technique to generate counterfactuals using QA framework and question generation model trained on original task data. |
| Outcome: | The proposed method improves performance on out-of-domain and challenging evaluation sets over and above existing methods. |
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
Copied to clipboard
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
| Challenge: | State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text. |
| Approach: | They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling. |
| Outcome: | The proposed model can be used to train sentences on language modeling tasks. |
Do Language Embeddings capture Scales? (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Pretrained Language Models possess significant linguistic, common sense and factual knowledge, but are short of the capability required for general common-sense reasoning. |
| Approach: | They propose to train pretrained language models with a method of canonicalizing numbers . they address a task which is also pre-requisite for general common-sense reasoning . |
| Outcome: | The proposed model can answer questions about common sense and linguistics, but lacks the capability to answer questions on scalar attributes. |
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models (2020.emnlp-demos)
Copied to clipboard
Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, Ann Yuan
| Challenge: | Existing tools for modeling and understanding models are limited . existing tools can assist practitioners in understanding and evaluating models . |
| Approach: | They present an open-source platform for visualization and understanding of NLP models. |
| Outcome: | The language interpretability tool (lit) is an open-source platform for visualization and understanding of NLP models. |
Towards Tracing Knowledge in Language Models Back to the Training Data (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Prior work on training data attribution (TDA) may offer effective tools for identifying such examples, known as "proponents". |
| Approach: | They propose a benchmark to identify which training examples taught an LM to generate a particular factual assertion. |
| Outcome: | The proposed methods have lower proponent-retrieval precision than baselines that do not have access to the LM. |