Chris Alberti
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Most recent coreference resolution systems use search algorithms over possible spans to identify mentions and resolve coreference. We instead present a coreference resolution system that uses a text-to-text (seq2seq) paradigm to predict mentions and links jointly, which simplifies the coreference resolution by eliminating both the search for mentions and coreferences. We implemented the coreference system as a transition system and use multilingual T5 as language model. We obtained state-of-the-art accuracy with 83.3 F1-score on the CoNLL-2012 data set. We use the SemEval-2010 data sets to evaluate on languages other than English and get substantially higher Zero-shot F1-scores for 3 out of 4 languages than previous approaches and significantly exceed previous supervised state-of-the-art results for all five tested languages.
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QAmeleon: Multilingual QA with Only 5 Examples
Fantine Huot
Sebastian Ruder
Mirella Lapata
Arxiv (2022)
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The availability of large, high-quality datasets has been one of the main drivers of recent progress in question answering (QA). Such annotated datasets however are difficult and costly to collect, and rarely exist in languages other than English, rendering QA technology inaccessible to underrepresented languages. An alternative to building large monolingual training datasets is to leverage pre-trained language models (PLMs) under a few-shot learning setting. Our approach, QAmeleon, uses a PLM to automatically generate multilingual data upon which QA models are trained, thus avoiding costly annotation. Prompt tuning the PLM for data synthesis with only five examples per language delivers accuracy superior to translation-based baselines, bridges nearly 60% of the gap between an English-only baseline and a fully supervised upper bound trained on almost 50,000 hand labeled examples, and always leads to substantial improvements compared to fine-tuning a QA model directly on labeled examples in low resource settings. Experiments on the TyDiQA-GoldP and MLQA benchmarks show that few-shot prompt tuning for data synthesis scales across languages and is a viable alternative to large-scale annotation.
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The paper presents an approach to semantic grounding of language models (LMs) that conceptualizes the LM as a conditional model generating text given a desired semantic message. It embeds the LM in an auto-encoder by feeding its output to a semantic parser whose output is in the same representation domain as the input message.
Compared to a baseline that generates text using greedy search, we demonstrate two techniques that improve the fluency and semantic accuracy of the generated text: The first technique samples multiple candidate text sequences from which the semantic parser chooses. The second trains the language model while keeping the semantic parser frozen to improve the semantic accuracy of the auto-encoder.
We carry out experiments on the English WebNLG 3.0 data set, using BLEU to measure the fluency of generated text and standard parsing metrics to measure semantic accuracy. We show that our proposed approaches significantly improve on the greedy search baseline. Human evaluation corroborates the results of the automatic evaluation experiments.
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Conciseness: An Overlooked Language Task
Aashish Kumar
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Abu Dhabi
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We report on novel investigations into training models that make sentences concise. We define the task and show that it is different from related tasks such as summarization and simplification. For evaluation, we release two test sets, consisting of 2000 sentences each, that were annotated by two and five raters, respectively. We demonstrate that conciseness is a difficult task for which zero-shot setups with giant neural language models often do not perform well. Given the limitations of these approaches, we propose a synthetic data generation method based on round-trip translations. Using this data to either train Transformers from scratch or fine-tune T5 models yields our strongest baselines that can be further improved by fine-tuning on an artificial conciseness dataset that we derived from multi-annotator machine translation test sets.
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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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ETC: Encoding Long and Structured Inputs in Transformers
Anirudh Ravula
Joshua Ainslie
Li Yang
Qifan Wang
Vaclav Cvicek
2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)
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Transformer models have advanced the state of the art in many NLP tasks. In this paper, we present a new Transformer architecture, Extended Transformer Construction (ETC), that addresses two key limitations of existing architectures, namely: scaling input length, and ingesting structured inputs. The main innovation is a new global-local attention mechanism between a global memory and the input tokens, which allows scaling attention to longer inputs. We show that combining global-local attention with relative position encodings and a Contrastive Predictive Coding (CPC) pre-training task allows ETC to naturally handle structured data. We achieve new state-of-the-art results on two natural language datasets requiring long and/or structured inputs.
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Big Bird: Transformers for Longer Sequences
Guru Prashanth Guruganesh
Joshua Ainslie
Anirudh Ravula
Qifan Wang
Li Yang
NeurIPS (2020)
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Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (in terms of memory mainly) on the sequence length due to their full attention mechanism. To remedy this, we propose, \emph{BigBird}, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that \emph{BigBird} is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis demonstrates the need for having an O(1) global tokens, such as CLS, that attend to the entire sequence as part of the sparse attentions. We show that the proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, \emph{BigBird} drastically improves performance on various NLP tasks such as question answering.
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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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Fusion of Detected Objects in Text for Visual Question Answering
Jeffrey Ling
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (2019), pp. 2131-2140
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To advance models of multimodal context‚ we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The “Bounding Boxes in Text Transformer” (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is highly effective on the Visual Commonsense Reasoning benchmark (visualcommonsense.org)‚ achieving a new state-of-the-art with a 25% relative reduction in error rate compared to published baselines and obtaining the best performance to date on the public leaderboard. A detailed ablation analysis shows that the early integration of the visual features into the text analysis is key to the effectiveness of the new architecture. A reference implementation of our models is provided as supplementary material.
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Natural Questions: a Benchmark for Question Answering Research
Olivia Redfield
Danielle Epstein
Illia Polosukhin
Matthew Kelcey
Jacob Devlin
Llion Jones
Ming-Wei Chang
Jakob Uszkoreit
Transactions of the Association of Computational Linguistics (2019) (to appear)
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We present the Natural Questions corpus, a question answering dataset. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations, 7,830 examples with 5-way annotations for development data, and a further 7,842 examples 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature.
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