Fernando Pereira
Fernando Pereira is VP and Engineering Fellow at Google, where he leads research and development in natural language understanding and machine learning. His previous positions include chair of the Computer and Information Science department of the University of Pennsylvania, head of the Machine Learning and Information Retrieval department at AT&T Labs, and research and management positions at SRI International. He received a Ph.D. in Artificial Intelligence from the University of Edinburgh in 1982, and has over 120 research publications on computational linguistics, machine learning, bioinformatics, speech recognition, and logic programming, as well as several patents. He was elected AAAI Fellow in 1991 for contributions to computational linguistics and logic programming, ACM Fellow in 2010 for contributions to machine learning models of natural language and biological sequences, and ACL Fellow for contributions to sequence modeling, finite-state methods, and dependency and deductive parsing. He was president of the Association for Computational Linguistics in 1993.
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Conversational Music Retrieval with Synthetic Data
Megan Eileen Leszczynski
Ravi Ganti
Shu Zhang
Arun Tejasvi Chaganty
Second Workshop on Interactive Learning for Natural Language Processing at NeurIPS 2022
Preview abstract
Users looking for recommendations often wish to improve suggestions through
broad natural language feedback (e.g., “How about something more upbeat?”).
However, building such conversational retrieval systems requires conversational
data with rich user utterances paired with slates of items that cover a diverse
range of preferences. This is challenging to collect scalably using conventional
methods like crowd-sourcing. We address this problem with a new technique to
synthesize high-quality dialog data by transforming the domain expertise encoded
in curated item collections into corresponding item-seeking conversations. The
method first generates a sequence of hypothetical slates returned by a system,
and then uses a language model to introduce corresponding user utterances. We
apply the approach on a dataset of curated music playlists to generate 10k diverse
music-seeking conversations. A qualitative human evaluation shows that a majority
of these conversations express believable sequences of slates and include user
utterances that faithfully express preferences for them. When used to train a
conversational retrieval model, the synthetic data yields up to a 23% relative gain
on standard retrieval metrics compared to baselines trained on non-conversational
and conversational datasets.
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Points, Paths, and Playscapes: Large-scale Spatial Language Understanding Tasks Set in the Real World
Daphne Luong
Proceedings of the First International Workshop on Spatial Language Understanding, Association for Computational Linguistics, New Orleans, Louisiana, USA(2018), pp. 46-52
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Spatial language understanding is important for practical applications and as a building block for better abstract language understanding. Much progress has been made through work on understanding spatial relations and values in images and texts as well as on giving and following navigation instructions in restricted domains. We argue that the next big advances in spatial language understanding can be best supported by creating large-scale datasets that focus on points and paths based in the real world, and then extending these to create online, persistent playscapes that mix human and bot players. The bot players can begin play having undergone a prior training regime, but then must learn, evolve, and survive according to their depth of understanding of scenes, navigation, and interactions.
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We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.
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Collective Entity Resolution with Multi-Focal Attention
Soumen Chakrabarti
Michael Ringaard
ACL(2016)
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Entity resolution is the task of linking each mention of an entity in text to the corresponding record in a knowledge base (KB). Coherence models for entity resolution encourage all referring expressions in a document to resolve to entities that are related in the KB. We explore attention-like mechanisms for coherence, where the evidence for each candidate is based on a small set of strong relations, rather than relations to all other entities in the document. The rationale is that document-wide support may simply not exist for non-salient entities, or entities not densely connected in the KB. Our proposed system outperforms state-of-the-art systems on the CoNLL 2003, TAC KBP 2010, 2011
and 2012 tasks.
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Plato: A Selective Context Model for Entity Resolution
Michael Ringgaard
Transactions of the Association for Computational Linguistics, 3(2015), pp. 503-515
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We present Plato, a probabilistic model for entity resolution that includes a novel approach for handling noisy or uninformative features,and supplements labeled training data derived from Wikipedia with a very large unlabeled text corpus. Training and inference in the proposed model can easily be distributed across many servers, allowing it to scale to over 10^7 entities. We evaluate Plato on three standard datasets for entity resolution. Our approach achieves the best results to-date on TAC KBP 2011 and is highly competitive on both the CoNLL 2003 and TAC KBP 2012 datasets.
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We describe Sparse Non-negative Matrix (SNM) language model estimation using multinomial loss on held-out data.
Being able to train on held-out data is important in practical situations where the training data is usually mismatched from the held-out/test data. It is also less constrained than the previous training algorithm using leave-one-out on training data: it allows the use of richer meta-features in the adjustment model, e.g. the diversity counts used by Kneser-Ney smoothing which would be difficult to deal with correctly in leave-one-out training.
In experiments on the one billion words language modeling benchmark, we are able to slightly improve on our previous results which use a different loss function, and employ leave-one-out training on a subset of the main training set. Surprisingly, an adjustment model with meta-features that discard all lexical information can perform as well as lexicalized meta-features. We find that fairly small amounts of held-out data (on the order of 30-70 thousand words) are sufficient for training the adjustment model.
In a real-life scenario where the training data is a mix of data sources that are imbalanced in size, and of different degrees of relevance to the held-out and test data, taking into account the data source for a given skip-/n-gram feature and combining them for best performance on held-out/test data improves over skip-/n-gram SNM models trained on pooled data by about 8% in the SMT setup, or as much as 15% in the ASR/IME setup.
The ability to mix various data sources based on how relevant they are to a mismatched held-out set is probably the most attractive feature of the new estimation method for SNM LM.
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Yedalog: Exploring Knowledge at Scale
Brian Chin
Vuk Ercegovac
Peter Hawkins
Mark S. Miller
Franz Och
Chris Olston
1st Summit on Advances in Programming Languages (SNAPL 2015), Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, pp. 63-78
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With huge progress on data processing frameworks, human programmers are frequently the bottleneck when analyzing large repositories of data. We introduce Yedalog, a declarative programming language that allows programmers to mix data-parallel pipelines and computation seamlessly in a single language. By contrast, most existing tools for data-parallel computation embed a sublanguage of data-parallel pipelines in a general-purpose language, or vice versa. Yedalog extends Datalog, incorporating not only computational features from logic programming, but also features for working with data structured as nested records. Yedalog programs can run both on a single machine, and distributed across a cluster in batch and interactive modes, allowing programmers to mix different modes of execution easily.
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Large Scale Distributed Acoustic Modeling With Back-off N-grams
Peng Xu
Thomas Richardson
IEEE Transactions on Audio, Speech and Language Processing, 21(2013), pp. 1158-1169
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The paper revives an older approach to acoustic modeling that borrows from n-gram language modeling in an attempt to scale up both the amount of training data and model size (as measured by the number of parameters in the model), to approximately 100 times larger than current sizes used in automatic speech recognition.
In such a data-rich setting, we can expand the phonetic context significantly beyond triphones, as well as increase the number of Gaussian mixture components for the context-dependent states that allow it. We have experimented with contexts that span seven or more context-independent phones, and up to 620 mixture components per state. Dealing with unseen phonetic contexts is accomplished using the familiar back-off technique used in language modeling due to implementation simplicity. The back-off acoustic model is estimated, stored and served using MapReduce distributed computing infrastructure.
Speech recognition experiments are carried out in an N-best list rescoring framework for Google Voice Search. Training big models on large amounts of data proves to be an effective way to increase the accuracy of a state-of-the-art automatic speech recognition system. We use 87,000 hours of training data (speech along with transcription) obtained by filtering utterances in Voice Search logs on automatic speech recognition confidence. Models ranging in size between 20--40 million Gaussians are estimated using maximum likelihood training. They achieve relative reductions in word-error-rate of 11% and 6% when combined with first-pass models trained using maximum likelihood, and boosted maximum mutual information, respectively. Increasing the context size beyond five phones (quinphones) does not help.
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Preview abstract
Google Voice Search is an application that provides a data-rich setup for both language and acoustic modeling research.
The approach we take revives an older approach to acoustic modeling that borrows from n-gram language modeling in an attempt to scale up both the amount of training data, and the model size (as measured by the number of parameters in the model), to approximately 100 times larger than current sizes used in automatic speech recognition.
Speech recognition experiments are carried out in an N-best list rescoring framework for Google Voice Search. We use 87,000 hours of training data (speech along with transcription) obtained by filtering utterances in Voice Search logs on automatic speech recognition confidence.
Models ranging in size between 20--40 million Gaussians are estimated using maximum likelihood training. They achieve relative reductions in word-error-rate of 11% and 6% when combined with first-pass models trained using maximum likelihood, and boosted maximum mutual information, respectively. Increasing the context size beyond five phones (quinphones) does not help.
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Distributed Acoustic Modeling with Back-off N-grams
Peng Xu
Thomas Richardson
Proceedings of ICASSP 2012, IEEE, pp. 4129-4132
Preview abstract
The paper proposes an approach to acoustic modeling that
borrows from n-gram language modeling in an attempt to
scale up both the amount of training data and model size (as
measured by the number of parameters in the model) to approximately 100 times larger than current sizes used in ASR. Dealing with unseen phonetic contexts is accomplished
using the familiar back-off technique used in language modeling due to implementation simplicity. The new acoustic
model is estimated and stored using the MapReduce distributed computing infrastructure. Speech recognition experiments are carried out in an Nbest rescoring framework for Google Voice Search. 87,000 hours of training data is obtained in an unsupervised fashion by filtering utterances in Voice Search logs on ASR confidence. The resulting models are trained using maximum likelihood and contain 20-40 million Gaussians. They achieve
relative reductions in WER of 11% and 6% over first-pass
models trained using maximum likelihood, and boosted MMI,
respectively.
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