Tom Kwiatkowski

I am a research scientist at Google where I work on computational Natural Language Understanding, with applications in search and question answering. Prior to this, I got my PhD at the University of Edinburgh and I did a Post-Doc at the University of Washington.
Authored Publications
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    1-Pager: One Pass Answer Generation and Evidence Retrieval
    Palak Jain
    The 2023 Conference on Empirical Methods in Natural Language Processing(2023) (to appear)
    Preview abstract We present 1-PAGER the first system that answers a question and retrieves evidence using a single Transformer-based model and decoding process. 1-PAGER incrementally partitions the retrieval corpus using constrained decoding to select a document and answer string, and we show that this is competitive with comparable retrieve-and-read alternatives according to both retrieval and answer accuracy metrics. 1-PAGER also outperforms the equivalent ‘closed-book’ question answering model, by grounding predictions in an evidence corpus. While 1-PAGER is not yet on-par with more expensive systems that read many more documents before generating an answer, we argue that it provides an important step toward attributed generation by folding retrieval into the sequence-to-sequence paradigm that is currently dominant in NLP. We also show that the search paths used to partition the corpus are easy to read and understand, paving a way forward for interpretable neural retrieval. View details
    NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders
    Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 (to appear)
    Preview abstract Neural document rerankers are extremely effective in terms of accuracy. However, the best models require dedicated hardware for serving, which is costly and often not feasible. To avoid this serving-time requirement, we present a method of capturing up to 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function that only requires 10-6% of the Transformer's FLOPs per document and can be served using commodity CPUs. When combined with a BM25 retriever, this approach matches the quality of a state-of-the art dual encoder retriever, that still requires an accelerator for query encoding. We introduce NAIL (Non-Autoregressive Indexing with Language models) as a model architecture that is compatible with recent encoder-decoder and decoder-only large language models, such as T5, GPT-3 and PaLM. This model architecture can leverage existing pre-trained checkpoints and can be fine-tuned for efficiently constructing document representations that do not require neural processing of queries. View details
    Preview abstract Large language models (LLMs) have shown impressive results across a variety of tasks while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribute the text that it generates is likely to be crucial for both system developers and users in this setting. We propose and study Attributed QA as a key first step in the development of attributed LLMs. We develop a reproducable evaluation framework for the task, using human annotations as a gold standard and a correlated automatic metric that we show is suitable for development settings. We describe and benchmark a broad set of architectures for the task. Our contributions give some concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third key question (How to build LLMs with attribution?). View details
    MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network
    Jan A. Botha
    Dan Bikel
    Andrew McCallum
    Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Association for Computational Linguistics, Online(2021), pp. 278-285
    Preview abstract We present an instance-based nearest neighbor approach to entity linking. In contrast to most prior entity retrieval systems which represent each entity with a single vector, we build a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions of different entities. This approach allows all mentions of an entity to serve as "class prototypes" as inference involves retrieving from the full set of labeled entity mentions in the training set and applying the nearest mention neighbor’s entity label. Our model is trained on a large multilingual corpus of mention pairs derived from Wikipedia hyperlinks, and performs nearest neighbor inference on an index of 700 million mentions. It is simpler to train, gives more interpretable predictions, and outperforms all other systems on two multilingual entity linking benchmarks. View details
    Preview abstract In this work, we present an entity linking model which combines a Transformer architecture with large scale pretraining from Wikipedia links. Our model achieves the state-of-the-art on two commonly used entity linking datasets: 96.7% on CoNLL and 94.9% on TAC-KBP. We present detailed analyses to understand what design choices are important for entity linking, including choices of negative entity candidates, Transformer architecture, and input perturbations. Lastly, we present promising results on more challenging settings such as end-to-end entity linking and entity linking without in-domain training data. View details
    Entities as Experts: Sparse Memory Access with Entity Supervision
    Thibault Févry
    Eunsol Choi
    EMNLP 2020 - Conference on Empirical Methods in Natural Language Processing (to appear)
    Preview abstract We focus on the problem of capturing declarative knowledge about entities in the learned parameters of a language model. We introduce a new model—Entities as Experts (EAE)— that can access distinct memories of the entities mentioned in a piece of text. Unlike previous efforts to integrate entity knowledge into sequence models, EAE’s entity representations are learned directly from text. We show that EAE’s learned representations capture sufficient knowledge to answer TriviaQA questions such as “Which Dr. Who villain has been played by Roger Delgado, Anthony Ainley, Eric Roberts?”, outperforming an encoder-generator Transformer model with 10× the parameters. According to the LAMA knowledge probes, EAE contains more factual knowledge than a similarly sized BERT, as well as previous approaches that integrate external sources of entity knowledge. Because EAE associates parameters with specific entities, it only needs to access a fraction of its parameters at inference time, and we show that the correct identification and representation of entities is essential to EAE’s performance. View details
    Preview abstract Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA, a question answering dataset covering 11 typologically diverse languages. Until recently, most multilingual research in natural language processing has been limited to machine translation or to technical tasks such as tagging and parsing. Question answering offers a scenario that is natural in that non-technical users intuitively understand the task, allowing high quality data collection in the absence of abundant annotators with expertise in both linguistics and the language of interest. This allows us select languages that are diverse with regard to their typology -- the set of linguistic features that each language expresses. We expect that models that can perform well on this set will generalize across a large number of the languages in the world. To encourage a more realistic distribution, the data is collected entirely in each native language without the use of translation (human or otherwise) and question creation is performed without seeing the answers. We present a quantitative analysis of the data quality, we provide example-level linguistic analyses and glosses of language phenomena that would not be found in English-only corpora, and we measure the performance of baseline systems. View details
    Preview abstract Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases. Motivated by the observation that efforts to code world knowledge into machine readable knowledge bases or human readable encyclopedias tend to be entity-centric, we investigate the use of a fill-in-the-blank task to learn context independent representations of entities from the text contexts in which those entities were mentioned. We show that large scale training of neural models allows us to learn high quality entity representations, and we demonstrate successful results on four domains: (1) existing entity-level typing benchmarks, including a 64% error reduction over previous work on TypeNet (Murty et al., 2018); (2) a novel few-shot category reconstruction task; (3) existing entity linking benchmarks, where we match the state-of-the-art on CoNLL-Aida without linking-specific features and obtain a score of 89.8% on TAC-KBP 2010 without using any alias table, external knowledge base or in domain training data and (4) answering trivia questions, which uniquely identify entities. Our global entity representations encode fine-grained type categories, such as Scottish footballers, and can answer trivia questions such as: Who was the last inmate of Spandau jail in Berlin? View details
    Preview abstract Language modeling tasks, in which words are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases. Motivated by the observation that efforts to code world knowledge into machine readable knowledge bases tend to be entity-centric, we investigate the use of a fill-in-the-blank task to learn context independent representations of entities from the contexts in which those entities were mentioned. We show that large scale training of neural models allows us to learn extremely high fidelity entity typing information, which we demonstrate with few-shot reconstruction of Wikipedia categories. Our learning approach is powerful enough to encode specialized topics such as Giro d'Italia cyclists. View details
    Preview abstract In this paper we study yes/no questions that are naturally occurring---meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that they are unexpectedly challenging. They often query for complex, non-factoid information, and require difficult entailment-like inference to solve. We also explore the effectiveness of a range of transfer learning baselines. We find that transferring from entailment data is more effective than transferring from paraphrase or extractive QA data, and that it, surprisingly, continues to be very beneficial even when starting from massive pre-trained language models such as BERT. Our best method trains BERT on MultiNLI and then re-trains it on our train set. It achieves 80.4% accuracy compared to 90% accuracy of human annotators (and 62% majority-baseline), leaving a significant gap for future work. View details