Daniel Andor
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Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
Pat Verga
Jianmo Ni
arXiv (2022)
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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?).
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QED: A Linguistically Principled Framework for Explainable Question Answering
Eunsol Choi
TACL (2021)
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A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility, and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks—post- hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.
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In this work we present Google’s submission to the BioASQ
7 biomedical question answering (QA) task (specifically Task 7b, Phase
B). The core of our systems are based on BERT QA models, specifically
the model of [1]. In this report, and via our submissions, we aimed to
investigate two research questions. We start by studying how domain
portable are QA systems that have been pre-trained and fine-tuned on
general texts, e.g., Wikipedia. We measure this via two submissions. The
first is a non-adapted model that uses a public pre-trained BERT model
and is fine-tuned on the Natural Questions data set [4]. The second system takes this non-adapted model and fine-tunes it with the BioASQ
training data. Next, we study the impact of error propagation in end-to-end retrieval and QA systems. Again we test this via two submissions.
The first uses human annotated relevant documents and snippets as input to the model and the second predicted documents and snippets. Our
main findings are that domain specific fine-tuning can benefit Biomedical QA. However, the biggest quality bottleneck is at the retrieval stage,
where we see large drops in metrics – over 10pts absolute – when using
non gold inputs to the QA model.
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Synthetic QA Corpora Generation with Roundtrip Consistency
Emily Pitler
Jacob Devlin
Association for Computational Linguistics (ACL), Florence, Italy (2019)
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We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency. By pretraining on the resulting corpora we obtain significant improvements on SQuAD2 and NQ, establishing a new state-of-the-art on the latter. Our synthetic data generation models, for both question generation and answer extraction, can be fully reproduced by finetuning a publicly available BERT model on the extractive subsets of SQuAD2 and NQ. We also describe a more powerful variant that does full sequence-to-sequence pretraining for question generation, obtaining exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2.
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Reading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers. We enable a BERT-based reading comprehension model to perform lightweight numerical reasoning. We augment the model with a predefined set of executable 'programs' which encompass simple arithmetic as well as extraction. Rather than having to learn to manipulate numbers directly, the model can pick a program and execute it. On the recent Discrete Reasoning Over Passages (DROP) dataset, designed to challenge reading comprehension models, we show a 33% absolute improvement by adding shallow programs. The model can learn to predict new operations when appropriate in a math word problem setting (Roy and Roth, 2015) with very few training examples.
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A Challenge Set and Methods for Noun-Verb Ambiguity
Ali Elkahky
Emily Pitler
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2562-2572
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English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite having achieved 97%+ accuracy on the WSJ Penn Treebank since 2002. These mistakes have been difficult to quantify and make taggers less useful to downstream tasks such as translation and text-to-speech synthesis. This paper creates a new dataset of over 30,000 naturally-occurring non-trivial examples of noun-verb ambiguity. Taggers within 1% of each other when measured on the WSJ have accuracies ranging from 57% to 75% accuracy on this challenge set. Enhancing the strongest existing tagger with contextual word embeddings and targeted training data improves its accuracy to 89%, a 14% absolute (52% relative) improvement. Downstream, using just this enhanced tagger yields a 28% reduction in error over the prior best learned model for homograph disambiguation for text-to-speech synthesis.
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Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
Ryan Mcdonald
Emily Pitler
Association for Computational Linguistics (ACL), Melbourne, Australia (2018)
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The rise of neural networks, and particularly recurrent neural networks, has produced significant advances in part-of-speech tagging accuracy. One characteristic common among these models is the presence of rich initial word encodings. These encodings typically are composed of a recurrent character-based representation with learned and pre-trained word embeddings. However, these encodings do not consider a context wider than a single word and it is only through subsequent recurrent layers that word or sub-word information interacts. In this paper, we investigate models that use recurrent neural networks with sentence-level context for initial character and word-based representations. In particular we show that optimal results are obtained by integrating these context sensitive representations through synchronized training with a meta-model that learns to combine their states. We present results on part-of-speech and morphological tagging with state-of-the-art performance on a number of languages.
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Linguistically-Informed Self-Attention for Semantic Role Labeling
Emma Strubell
Pat Verga
Andrew McCallum
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (2018)
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Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text.
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Real-Time Loop Closure in 2D LIDAR SLAM
Wolfgang Hess
Holger Rapp
2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271-1278
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Portable laser range-finders, further referred to as LIDAR, and simultaneous localization and mapping (SLAM) are an efficient method of acquiring as-built floor plans. Generating and visualizing floor plans in real-time helps the operator assess the quality and coverage of capture data. Building a portable capture platform necessitates operating under limited computational resources. We present the approach used in our backpack mapping platform which achieves real-time mapping and loop closure at a 5 cm resolution. To achieve real-time loop closure, we use a branch-and-bound approach for computing scan-to-submap matches as constraints. We provide experimental results and comparisons to other well known approaches which show that, in terms of quality, our approach is competitive with established techniques.
IEEE-copyrighted article, http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7487258
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Globally Normalized Transition-Based Neural Networks
Association for Computational Linguistics (2016)
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We introduce a globally normalized transition-based neural network
model that achieves state-of-the-art part-of-speech tagging,
dependency parsing and sentence compression results. Our model is a
simple feed-forward neural network that operates on a task-specific
transition system, yet achieves comparable or better accuracies than
recurrent models.
We discuss the importance of global as opposed to local normalization:
a key insight is that the label bias problem implies that
globally
normalized models can be strictly more expressive
than locally normalized models.
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