We present RICHES, a novel approach that interleaves retrieval with sequence generation tasks.RICHES offers an alternative to conventional RAG systems by eliminating the need for separate retriever and generator.It retrieves documents by directly decoding their contents, constrained on the corpus.Unifying retrieval with generation allows us to adapt to diverse new tasks via prompting alone.RICHES can work with any Instruction-tuned model, without additional training.It provides attributed evidence, supports multi-hop retrievals and interleaves thoughts to plan on what to retrieve next, all within a single decoding pass of the LLM.We demonstrate the strong performance of RICHES across ODQA tasks including attributed and multi-hop QA.

Benjamin Muller, John Wieting, Jonathan Clark, Tom Kwiatkowski, Sebastian Ruder, Livio Soares, Roee Aharoni, Jonathan Herzig, Xinyi Wang. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

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-ofthe 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.

Abstract Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. We isolate and define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. We describe an annotation procedure, collect data on the Wikipedia corpus, and use the data to train models to automatically decontextualize sentences. We present preliminary studies that show the value of sentence decontextualization in a user-facing task, and as preprocessing for systems that perform document understanding. We argue that decontextualization is an important subtask in many downstream applications, and that the definitions and resources provided can benefit tasks that operate on sentences that occur in a richer context.

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 encodergenerator 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. * Work done during Google AI residency.

Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA—a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology—the set of linguistic features each language expresses—such that we expect models performing well on this set to generalize across a large number of the world’s languages. We present a quantitative analysis of the data quality and example-level qualitative linguistic analyses of observed language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don’t know the answer yet, and the data is collected directly in each language without the use of translation.

Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query, which is computationally prohibitive. In this paper, we introduce query-agnostic indexable representations of document phrases that can drastically speed up open-domain QA. In particular, our dense-sparse phrase encoding effectively captures syntactic, semantic, and lexical information of the phrases and eliminates the pipeline filtering of context documents. Leveraging strategies for optimizing training and inference time, our model can be trained and deployed even in a single 4-GPU server. Moreover, by representing phrases as pointers to their start and end tokens, our model indexes phrases in the entire English Wikipedia (up to 60 billion phrases) using under 2TB. Our experiments on SQuAD-Open show that our model is on par with or more accurate than previous models with 6000x reduced computational cost, which translates into at least 68x faster end-to-end inference benchmark on CPUs. Code and demo are available at nlp.cs.washington.edu/denspi

General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction.Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph.However, both of these approaches are limited in their ability to generalize.In this paper, we build on extensions of Harris' distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text.We show that these representations significantly outperform previous work on exemplar based relation extraction (FewRel) even without using any of that task's training data.We also show that models initialized with our task agnostic representations, and then tuned on supervised relation extraction datasets, significantly outperform the previous methods on Se-mEval 2010 Task 8, KBP37, and TACRED.

We present the Natural Questions corpus, a question answering data set. 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 with 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.

We analyze human’s disagreements about the validity of natural language inferences. We show that, very often, disagreements are not dismissible as annotation “noise”, but rather persist as we collect more ratings and as we vary the amount of context provided to raters. We further show that the type of uncertainty captured by current state-of-the-art models for natural language inference is not reflective of the type of uncertainty present in human disagreements. We discuss implications of our results in relation to the recognizing textual entailment (RTE)/natural language inference (NLI) task. We argue for a refined evaluation objective that requires models to explicitly capture the full distribution of plausible human judgments.

We formalize a new modular variant of current question answering tasks by enforcing complete independence of the document encoder from the question encoder.This formulation addresses a key challenge in machine comprehension by requiring a standalone representation of the document discourse.It additionally leads to a significant scalability advantage since the encoding of the answer candidate phrases in the document can be pre-computed and indexed offline for efficient retrieval.We experiment with baseline models for the new task, which achieve a reasonable accuracy but significantly underperform unconstrained QA models.We invite the QA research community to engage in Phrase-Indexed Question Answering (PIQA, pika) for closing the gap.The leaderboard is at: nlp.

The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast—partly due to the lack of a strong type system—dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages. However, the lack of a type system makes a formal mechanism for deriving logical forms from dependency structures challenging. We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees. These logical forms are then used for semantic parsing of natural language to Freebase. Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task. Compared to prior work, we obtain the strongest result to date on Free917 and competitive results on WebQuestions.

Eunsol Choi, Tom Kwiatkowski, Luke Zettlemoyer. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

In this paper, we demonstrate that significant performance gains can be achieved in CCG semantic parsing by introducing a linguistically moti-vated grammar induction scheme. We present a new morpho-syntactic fac-tored lexicon that models systematic variations in morphology, syntax, and semantics across word classes. The grammar uses domain-independent facts about the English language to restrict the number of incorrect parses that must be considered, thereby enabling effective learning from less data. Experiments in benchmark domains match previous models with one quarter of the data and provide new state-of-the-art results with all available data, including up to 45% relative test-error reduction. 1

We consider the challenge of learning semantic parsers that scale to large, open-domain problems, such as question answering with Freebase.In such settings, the sentences cover a wide variety of topics and include many phrases whose meaning is difficult to represent in a fixed target ontology.For example, even simple phrases such as 'daughter' and 'number of people living in' cannot be directly represented in Freebase, whose ontology instead encodes facts about gender, parenthood, and population.In this paper, we introduce a new semantic parsing approach that learns to resolve such ontological mismatches.The parser is learned from question-answer pairs, uses a probabilistic CCG to build linguistically motivated logicalform meaning representations, and includes an ontology matching model that adapts the output logical forms for each target ontology.Experiments demonstrate state-of-the-art performance on two benchmark semantic parsing datasets, including a nine point accuracy improvement on a recent Freebase QA corpus.

We present a novel approach for improving communication success between users of speech-to-speech translation systems by automatically detecting errors in the output of automatic speech recognition (ASR) and statistical machine translation (SMT) systems. Our approach initiates system-driven targeted clarification about errorful regions in user input and repairs them given user responses. Our system has been evaluated by unbiased subjects in live mode, and results show improved success of communication between users of the system.

This paper addresses the problem of detecting words that are out-of-vocabulary (OOV) for a speech recognition system to improve automatic speech translation. The detection system leverages confidence prediction techniques given a confusion network representation and parsing with OOV word tokens to identify spans associated with true OOV words. Working in a resource-constrained domain, we achieve OOV detection F-scores of 60-66 and reduce word error rate by 12% relative to the case where OOV words are not detected.