Peter Plantinga on Mispronunciation Detection for Kids’ Speech

Real-time Mispronunciation Detection for Kids’ Speech

Modern mispronunciation detection and diagnosis systems have seen significant gains in accuracy due to the introduction of deep learning. However, these systems have not been evaluated for the ability to be run in real-time, an important factor in applications that provide rapid feedback. In particular, the state-of-the-art uses bi-directional recurrent networks, where a uni-directional network may be more appropriate. Teacher-student learning is a natural approach to improve a uni-directional model, but when using a CTC objective, this is limited by poor alignment of outputs to evidence. We address this limitation by trying two loss terms for improving the alignments of our models. One loss is an “alignment loss” term that encourages outputs only when features do not resemble silence. The other loss term uses a uni-directional model as teacher model to align the bi-directional model. Our proposed model uses these aligned bi-directional models as teacher models. Experiments on the CSLU kids’ corpus show that these changes decrease the latency of the outputs, and improve the detection rates, with a trade-off between these goals.

Clippers 9/24: Marie de Marneffe on Speaker Commitment

Do you know that there’s still a chance? Identifying speaker commitment for natural language understanding

Marie-Catherine de Marneffe

When we communicate, we infer a lot beyond the literal meaning of the words we hear or read. In particular, our understanding of an utterance depends on assessing the extent to which the speaker stands by the event she describes. An unadorned declarative like “The cancer has spread” conveys firm speaker commitment of the cancer having spread, whereas “There are some indicators that the cancer has spread” imbues the claim with uncertainty. It is not only the absence vs. presence of embedding material that determines whether or not a speaker is committed to the event described: from (1) we will infer that the speaker is committed to there being war, whereas in (2) we will infer the speaker is committed to relocating species not being a panacea, even though the clauses that describe the events in (1) and (2) are both embedded under “(s)he doesn’t believe”.

(1) The problem, I’m afraid, with my colleague here, he really doesn’t believe that it’s war.

(2) Transplanting an ecosystem can be risky, as history shows. Hellmann doesn’t believe that relocating species threatened by climate change is a panacea.

In this talk, I will first illustrate how looking at pragmatic information of what speakers are committed to can improve NLP applications. Previous work has tried to predict the outcome of contests (such as the Oscars or elections) from tweets. I will show that by distinguishing tweets that convey firm speaker commitment toward a given outcome (e.g., “Dunkirk will win Best Picture in 2018”) from ones that only suggest the outcome (e.g., “Dunkirk might have a shot at the 2018 Oscars”) or tweets that convey the negation of the event (“Dunkirk is good but not academy level good for the Oscars”), we can outperform previous methods. Second, I will evaluate current models of speaker commitment, using the CommitmentBank, a dataset of naturally occurring discourses developed to deepen our understanding of the factors at play in identifying speaker commitment. We found that a linguistically informed model outperforms a LSTM-based one, suggesting that linguistic knowledge is needed to achieve robust language understanding. Both models however fail to generalize to the diverse linguistic constructions present in natural language, highlighting directions for improvement.

Clippers 9/10: Michael White on Constrained Decoding in Neural NLG

Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue

(joint work with Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani and Rajen Subba)

Neural methods for natural language generation (NNLG) arrived with much fanfare a few years ago and became the dominant method employed in the recent E2E NLG Challenge. While neural methods promise flexible, end-to-end trainable models, recent studies have revealed their inability to produce satisfactory output for longer or more complex texts as well as how the black-box nature of these models makes them difficult to control. In this talk, I will propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning. I will then introduce a constrained decoding approach for sequence-to-sequence models that leverages this representation to improve semantic correctness. Finally, I will demonstrate promising results on a new conversational weather dataset as well as the E2E dataset and discuss remaining challenges.

Clippers 9/3: Cory Shain on Linguistic Prediction Effects in fMRI

Title: fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Abstract: Much research in cognitive neuroscience supports prediction as a canonical computation of cognition in many domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain’s response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found we found effects of prediction measures in the language network but not in the domain-general, multiple-demand network. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.