Alexander Zotov


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Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing
Akshat Shrivastava | Pierce Chuang | Arun Babu | Shrey Desai | Abhinav Arora | Alexander Zotov | Ahmed Aly
Findings of the Association for Computational Linguistics: EMNLP 2021

An effective recipe for building seq2seq, non-autoregressive, task-oriented parsers to map utterances to semantic frames proceeds in three steps: encoding an utterance x, predicting a frame’s length |y|, and decoding a |y|-sized frame with utterance and ontology tokens. Though empirically strong, these models are typically bottlenecked by length prediction, as even small inaccuracies change the syntactic and semantic characteristics of resulting frames. In our work, we propose span pointer networks, non-autoregressive parsers which shift the decoding task from text generation to span prediction; that is, when imputing utterance spans into frame slots, our model produces endpoints (e.g., [i, j]) as opposed to text (e.g., “6pm”). This natural quantization of the output space reduces the variability of gold frames, therefore improving length prediction and, ultimately, exact match. Furthermore, length prediction is now responsible for frame syntax and the decoder is responsible for frame semantics, resulting in a coarse-to-fine model. We evaluate our approach on several task-oriented semantic parsing datasets. Notably, we bridge the quality gap between non-autogressive and autoregressive parsers, achieving 87 EM on TOPv2 (Chen et al. 2020). Furthermore, due to our more consistent gold frames, we show strong improvements in model generalization in both cross-domain and cross-lingual transfer in low-resource settings. Finally, due to our diminished output vocabulary, we observe 70% reduction in latency and 83% reduction in memory at beam size 5 compared to prior non-autoregressive parsers.


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Task-Oriented Dialogue as Dataflow Synthesis
Jacob Andreas | John Bufe | David Burkett | Charles Chen | Josh Clausman | Jean Crawford | Kate Crim | Jordan DeLoach | Leah Dorner | Jason Eisner | Hao Fang | Alan Guo | David Hall | Kristin Hayes | Kellie Hill | Diana Ho | Wendy Iwaszuk | Smriti Jha | Dan Klein | Jayant Krishnamurthy | Theo Lanman | Percy Liang | Christopher H. Lin | Ilya Lintsbakh | Andy McGovern | Aleksandr Nisnevich | Adam Pauls | Dmitrij Petters | Brent Read | Dan Roth | Subhro Roy | Jesse Rusak | Beth Short | Div Slomin | Ben Snyder | Stephon Striplin | Yu Su | Zachary Tellman | Sam Thomson | Andrei Vorobev | Izabela Witoszko | Jason Wolfe | Abby Wray | Yuchen Zhang | Alexander Zotov
Transactions of the Association for Computational Linguistics, Volume 8

We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. Programs include metacomputation operators for reference and revision that reuse dataflow fragments from previous turns. Our graph-based state enables the expression and manipulation of complex user intents, and explicit metacomputation makes these intents easier for learned models to predict. We introduce a new dataset, SMCalFlow, featuring complex dialogues about events, weather, places, and people. Experiments show that dataflow graphs and metacomputation substantially improve representability and predictability in these natural dialogues. Additional experiments on the MultiWOZ dataset show that our dataflow representation enables an otherwise off-the-shelf sequence-to-sequence model to match the best existing task-specific state tracking model. The SMCalFlow dataset, code for replicating experiments, and a public leaderboard are available at